Men generally have higher rates of suicide, despite fewer overt indicators of risk. Differences in presentation and response suggest a need to better understand why suicide prevention is less effective for men.To explore the views of at‐risk men, friends and family about the tensions inherent in suicide prevention and to consider how prevention may be improved.Secondary analysis of qualitative interview and focus group data, using thematic analysis techniques, alongside bracketing, construction and contextualisation.A total of 35 men who had recently made a suicide attempt participated in interviews, and 47 family and friends of men who had made a suicide attempt took part in focus groups. Participants recounted their experiences with men's suicide attempts and associated interventions, and suggested ways in which suicide prevention may be improved.Five tensions in perspectives emerged between men and their support networks, which complicated effective management of suicide risk: (i) respecting privacy vs monitoring risk, (ii) differentiating normal vs risky behaviour changes, (iii) familiarity vs anonymity in personal information disclosure, (iv) maintaining autonomy vs imposing constraints to limit risk, and (v) perceived need for vs failures of external support services.Tension between the different perspectives increased systemic stress, compounding problems and risk, thereby decreasing the effectiveness of detection of and interventions for men at risk of suicide. Suggested solutions included improving risk communication, reducing reliance on single source supports and increasing intervention flexibility in response to individual needs. Women exhibit higher rates of deliberate self‐harm,It has been proposed that women's behaviours have shaped the way suicidal presentations are perceived, resulting in lower rates of detection and intervention among suicidal men. However, men are also vulnerable to some common risk factors, with their presentations also framed by their specific sociocultural context.In addition, men tend to select more violent means,These findings point to the need to better understand the factors specific to suicide prevention in at‐risk males from the people involved, that is, the at‐risk men themselves and their friends and families. While suicide prevention programmes are available, there are questions about the effectiveness of such programmes over timeOur recently completed qualitative study of at‐risk men and their family/friends used thematic analysisTo our knowledge, this is the first qualitative study of its kind involving both at‐risk men and their friends/families after a suicide attempt, in order to gain an in‐depth understanding of the complexities and divergent views involved in effective prevention of suicide in at‐risk males.22.1The results are based on the principles of qualitative secondary analysis (QSA), which allows for “expanding understanding of a particular phenomenon”2.2Two groups of participants were recruited in all states and territories across Australia: (i) adult men who had made a suicide attempt in the previous 6‐18 months, for a face‐to‐face interview, and (ii) adult family and friends of men who had made an attempt in the same time frame, for participation in focus group discussions. The 6‐ to 18‐month time frame was selected to minimise risk of triggering distress, while enabling detailed recall of experiences. To gain a breadth of views, family and friends were not necessarily related to the men interviewed.The study was publicised through local, state and national mental health organisations, professional associations and community networks. Recruitment was extremely difficult, due in part to the stigma often accompanying suicidality, lack of awareness by some friends/family that a suicide attempt had taken place and, restrictions from the 6‐ to 18‐month time frame. Respondents were screened for suitability on the basis of current mental health and selection criteria, and those who participated were reimbursed $50AUD.2.3Prior to interviews and focus groups, participants completed the Patient Health Questionnaire‐9 (PHQ‐9)2.4Semi‐structured interview and focus group schedules consisting of open‐ended questions explored participants’ experiences with suicide attempts in men, including factors contributing to a suicide attempt, as well as perceived barriers to preventing suicide. All interviews and focus group discussions were conducted by a male member of the research team (MP) and lasted 45‐70 and 60‐90 minutes, respectively. With consent from participants, they were recorded and transcribed verbatim. No participants withdrew from the study.2.5A risk management process was implemented throughout the project. Participants were monitored for signs of distress during the interviews/focus groups and were identified as “at risk” if they reported (i) a drop in mood after the interview/group discussion, which was assessed using a visual analogue scale where participants self‐rated how they were feeling before commencing and again at the close of the interview/focus groupAs a standard part of the protocol, all participants received contact details for mental health services in their region and, in the week after participation, a phone call by the research team to check whether any distress had been experienced.2.6Digital recordings were transcribed, de‐identified and analysed using the NVivo qualitative data analysis software.Several strategies were employed to reduce potential researcher bias, based on existing recommendations.The Human Research Ethics Committee of the University of New South Wales granted ethical approval for the study (HREC 13077).33.1M=8.0, SD=6.2 and M=5.5, SD=5.7, respectively) and in the minimal to normal range on the GAD . A majority of men (71%) and family/friends (87%) reported not experiencing suicidal ideation in the 2 weeks prior to interviews.Thirty‐five men completed one‐to‐one interviews  and forty‐seven family and friends, including twenty‐six women (55%), participated in eight focus groups . About one‐third (34%) of men were currently employed, 46% were unable to work and 20% unemployed, studying or retired. Just over half (54%) had never married, 11% were currently married and 35% were separated or divorced. Approximately half of the family/friends were currently employed (53%), with 19% unable to work and 27% unemployed, studying or retired. About half (49%) were currently married or in a de facto relationship, 28% never married, and 23% separated, divorced or widowed. Men's and family/friends’ PHQ‐9 scores fell within the mild depression range With my closest friends it was, ‘I don't want you to know how I feel’. I'm a dad of three kids and a husband. I've got a good job. I don't want you to know that I'm so sad that I cry at red lights. Accurately monitoring mood and risky behaviour was identified by the majority of family/friends as critical to supporting and managing mood problems and suicidality. However, the men tended to perceive frequent inquiries about mood as invasive, irritating, and patronising or, a challenge to their sense of self. For example, one man reported:…they tend to say they would be better if you weren't pestering them by trying to get in contact with their feelings or their emotions… For another, his sense of masculine pride underlined his need for privacy, which was further reflected by one focus group member (FGM) who observed:When I reviewed the death of one of my colleagues, one of the things was that he changed his behaviour. He was not known for returning phone calls within a reasonable time. He might take three or four days, but then he got to almost a week and a half, two weeks…and that was a change of behaviour. On the other hand, checking‐in by family/friends was necessary for noticing warning signs, especially in men who didn't communicate their feelings or tended to isolate themselves when becoming more depressed or irritable. For example, this particular family/friend described retrospectively recognising changes in the behaviour of a colleague who had died by suicide:I've had so many hard times…there's a point where there's so much recurrence that you feel that you're putting them through it a lot. And that you're not making any headway yourself…it's just to shield them, to keep them away from it because I don't feel comfortable putting them in that position Several negative outcomes were reported to result from this tension between respecting the man's privacy vs the need to be vigilant in risk monitoring. Often, anxiety increased, due to perceived loss of control by the individual or carers, and rapport between them was damaged. Some men became more reclusive and less likely to disclose their thoughts or distress. Even when men were relatively open about their suicidal plans, awareness of on‐going risk tended to increase stress and anxiety within the relationship, especially if they were conscious of being a burden:Individuals and focus group members suggested a number of potential solutions to address this tension. Regular monitoring was seen by the majority of participants to be essential, despite the additional stress it may generate. Negative impacts may be ameliorated, however, by listening without judgement, providing at‐risk men with information about support services outside the family, or, with consent, by sharing information with other people in contact with the individual.3.3.2…and I yell at someone and bump into somebody else on the way out, if the [person had] said, ‘gee, it's not like [name]’ that would've helped too, but nobody chased me down the corridor to the doorway to say, ‘[name], come back. I want to talk to you’. That would've helped. Both at‐risk men and family/friends agreed that an important part of monitoring risk involved accurately recognising changes in behaviour. However, a second tension related to the difficulty in differentiating non‐harmful behaviour change from change indicating mood disruption and increased risk of suicidality. For example, one man related mood disruptions and missed opportunities for somebody to check in:The other thing that I found difficult was to work out what was normal teenage behaviour and what was actually locking himself away because of being down…’is that suicidal behaviour, or him being a teenager?’ Yet friends and family reported an inability to recognise these instances for what they were:We didn't probably take it serious enough when he was asking for help. And it's a horrible thing to say, but we all work and we're all busy and you've all got your own life and at the time I was just, you know, I'm busy, leave me alone. I shouldn't say that but I did. I felt guilt for a very long time after that I didn't give him more attention. Participants noted that this tension was particularly relevant for adolescents, who often engaged in riskier behaviour, were more irritable and emotionally reactive and sought greater autonomy and privacy. However, even among adult men, behaviour change was also affected by things other than suicidality, such as unrelated relationship breakdowns. Conversely, statements of suicidal ideation or intent may be misinterpreted or judged to be lacking in veracity by family and friends:It's one of those friction points, isn't it? Yeah. ‘Cause in a way I'm sabotaging my own ability to get better. And they're trying to find this way of helping me. And I'm just not helping. Again, family/friends reported that the difficulties of accurately evaluating behavioural changes often led to either “false positives” contributing to conflict or “false negatives” resulting in insufficient support and self‐harm. Conversely, men acknowledged an inability to clearly communicate the risk:They highlighted the importance of consultation with general practitioners, clinical psychologists or counsellors, to decrease risk and anxiety associated with this tension.3.3.3I was aware that that wasn't his standard way of living, and it became an obvious sign. I do understand that it was only, though, because I actually knew the person for a longer period of time. Whereas… earlier on in that friendship or at an acquaintance level you wouldn't have the same understanding… You would just assume them to be a joker or a larrikin. The relative benefit of risk assessment and monitoring carried out by people familiar to the man at risk, vs by independent individuals, such as health professionals, also emerged as a tension. On the one hand, familiar individuals were often better able to recognise and interpret idiosyncratic changes in behaviour:And I remember breaking down in the doctor's surgery. I was there just for an annual check‐up and as soon as he closed the door I was a mess…I wouldn't allow myself to show it to friends and family. It was to a stranger where it was kind of like you felt that if you were going to be judged it would be far less than what it would be from family and friends. However, in some instances, greater familiarity also made listening without judgement more difficult and reduced the likelihood of disclosure of important information. This was particularly true for those men who prioritised independent problem‐solving or did not want to be perceived differently after disclosing “weaknesses.” An independent person on the other hand could provide a different point of view, act as a “circuit breaker,” or bring clarity based on professional detachment. Anonymity and perceived freedom from judgement made it easier for men to open up about problems and feelings:However, at the same time, outside observers were less likely to identify idiosyncratic behavioural cues, signs of deception or behavioural change.Some participants reported that this tension resulted in increased stress and perceived loss of control for parties, as well as ineffective risk monitoring and management. Simply choosing the wrong time to approach men, for example, could damage rapport or lead to inaccurate risk assessment, despite some men expressing a desire for others to notice changes in their behaviour or demeanour. One solution suggested by participants was to ensure that individuals are aware of and linked into both familiar and independent support systems during times of distress.3.3.4A majority of participants identified managing risky behaviour as another critical aspect of supporting at‐risk men—as distinct from risk monitoring. The extent to which a man's autonomy was respected or constrained during this process represented a fourth complexity to be navigated. On one hand, family and friends reported that challenging his unhelpful thoughts and restricting his behaviour was often essential to keeping a man alive and safe, such as when intent to self‐harm was active and strong. However, removing a man's freedom to choose could put strain on relationships, trigger blame and distress and cause conflict.I know from my own personal experience with my dad, he won't accept the help really. I could set up a hundred different things, to be honest, but he'll say, no, I don't need it… There was no way I could make him even go and see his GP. So, it's a struggle when they put the wall up…he just kept saying, ‘no, no, no’ all the time. Family/friends reported that men were often difficult to reach, describing various instances in which men expressed reluctance to engage with support, or refused to accept the type of care being offered. For example:…by the time I got down there, they've already got him off the side of the road… they got him in the police car and took him home. And he actually took a few swipes at the copper. A good guy, he just let go. Trying to help him was really hard. In addition, managing risk sometimes required family, friends or services to impose limits on men's choices in order to prevent harm, for example, restricting movement or access to potentially harmful materials or involuntary placement in inpatient health services. One participant described observing police intervene with a suicidal man:Similarly, several of the men argued that decision‐making by acutely suicidal men was often affected by impulsiveness, and lack of systematic reasoning or insight into other available options (such as accessing treatment or contact with support groups). As such, the majority of all participants agreed that managing risk of suicide sometimes required acting against the immediate wishes of individuals. Several participants argued that support should nevertheless attempt to improve perceived self‐efficacy by minimising unnecessary behavioural restrictions.3.43.4.1A final tension concerned the role of external services in managing risk of further suicide attempts. A majority of men and focus group participants expressed frustration at or criticism of welfare or health services supporting individuals at risk. However, participants also identified that services often had capacity to manage risk in ways not available within normal family and social networks.I took him to the doctor and they sent him to mental health, who put him in hospital to try and dry him out, which was a disaster. They ended up calling the police and they threw him down the hallway and handcuffed him, and he couldn't cope. He didn't get the right sort of help … And, that made him a lot worse. Then he just decided that he didn't want help anymore, that he would be fine, everybody just leave him alone… And he just kept escalating down, down, down. One bad experience is perceived as, ‘well, you are all the same’ and will lead to not seeking help again, almost suicide just to spite them. Participants described various perceived failures of health or other services related to: assessment of mood disorder and suicidality, scope or quality of intervention, and the extent and clarity of communication with family members. Participants reported that these failures tended to damage relationships and faith in services, making on‐going support more difficult. One family/friend participant recounted how a failed intervention amplified their family member's hopelessness:I suppose I used more of what was actually out there than a lot of people did. A lot of people don't know what services are out there for those sort of things… psychologists, psychiatrists, counsellors, they're great, especially in a mental health plan. I thought I've got nothing to lose, I'll give this [psychologist] a go. There's some sense in what these guys are saying and they're not counsellors, they're not trying to tell me to smile and be happy. They're showing me the mechanisms of what's going on and I saw some sense in that, that we could change this… On the other hand, participants reported that health services sometimes played a critical role in supporting individuals at risk. This was due in part to their capacity to manage and contain risk more directly than family or friends, often indirectly helping supporters to deal with the stress.At times, simply changing the environment around a man acted as another useful “circuit breaker,” for example by generating a community around otherwise isolated individuals. This may be particularly important when supporting those who had alienated themselves through aggression, substance abuse or other avoidant behaviours.Participants observed that frustration, confusion and distrust directed at the service systems tended to reduce the effectiveness of co‐operation between care providers and thereby the quality and consistency of support for individuals at risk. Several focus group members suggested that providing skills training and psycho‐education to families and friends early in interventions would improve support to individuals at risk, as well as provide an understanding of how service systems operate, reducing reliance on and resentment towards relevant services.4The tensions identified here had several negative impacts. Not only did they contribute to greater difficulty in family/friends identifying early warning signs of an impending suicide attempt, they also had the effect of limiting requests for help by suicidal men. However, the findings point to suggestions for improving interventions addressing suicidal behaviours in men and for enhancing health‐care providers’ approaches to working with affected family and friends. In particular, three areas emerged as targets for change: first, inadequacies in the way individuals or supports communicate risk information, behaviour or motives. Second, the lack of understanding, by both men and family/friends, of the processes, risk factors or warning signs. Third, the lack of flexibility in the delivery of support, by either family/friends or services, in response to the needs or preferences of individuals .Approaches to managing these tensions and for enhancing existing clinical interventions may need to incorporate the qualitative results presented here, as previously advocated.4.1Factors limiting men's willingness or skills to disclose important risk‐related information may be assisted by improving ease of risk reporting and asking for help, as well as men's self‐care skills. For example, individuals, family and friends, and indeed the public at large would benefit from psycho‐education about available support services within the community and effective self‐care strategies, alongside information about the tensions that might be expected in the course of managing suicide risk. Men would benefit from understanding alternatives for reporting distress or suicidal thinking, while at the same time keeping in mind personal preferences about confidentiality.While not specifically suggested by the majority of men, it may be worth considering a more resource‐intensive process, which involves adapting or creating specialised services for male suicide support, alongside existing but indirectly related support networks, such as Men's Sheds.Likewise, there is a need to raise awareness about the tensions identified here in existing public education campaigns that go beyond just at‐risk men and their family/friends, particularly with regard to promoting the message that these tensions are to be expected yet are surmountable. This concords with recent research that suggests a more nuanced understanding of how men access care, with a specific emphasis on the notion that responsibility for accessing services should not rest solely on a suicidal individual.4.2Both participant groups consistently reported that reliance on any one individual or group for risk monitoring increased systemic anxiety and risk, particularly given men's reluctance to seek help. This was particularly evident in tensions 1, 2 and 3, where men and their family and friends struggled with monitoring risk, over‐stepping boundaries, understanding what constituted an agreed threshold for action, and what might happen when service use was initiated. However, this concern appeared to be lower in cases where support was effectively integrated, such as through sharing information, whether amongst family members or health‐care professionals, and where loss of status was not perceived to be at stake, due to lack of judgement on behalf of the support person.Skills training, consultation or psycho‐education about how service systems operate for men may reduce reluctance to access resources and increase ease of access, which may be particularly valuable if accessed by men during an earlier phase of distress. Use of online self‐help programmes on suicide prevention and/or men's mental health such as the myCompass programme4.3Managing risk may at times require acting against the explicit choices of individuals; however, our participants suggested that the effectiveness of interventions may be maximised by avoiding unnecessary restriction, involving men as much as possible in decision making and flexibly adapting to individuals’ preferences.Taking some responsibility for choosing to live, or working to change unhelpful behaviours, rather than maintaining total reliance on external forces to prevent men from hurting themselves was also judged to be essential. Providing this message to at‐risk men may help them to change unhelpful thinking patterns, especially when offered in the context of continued support from family/friends, belonging and connectedness. However, if pressured inappropriately, it may add to their feelings of distress or incompetence.Conversely, men may choose to take responsibility for their care, in which case, effort placed on “skilling up” men with regard to self‐regulation and coping may allow them to address problems with independence from support networks, at the same time enhancing self‐esteem, self‐efficacy and promoting long‐term change. It appears important to access available resources within care systems flexibly or adaptively, considering specific risks, needs and preferences within the system.4.4The study is potentially limited in that a majority of the sample were aged 40 years or more, there were no men aged over 67 years, and a majority were not in current partnerships. Australian data5Previous research has shown that there are considerable gender differences in suicidal risks and behaviour.


Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: medication, symptom, and background. Each sentence is projected into an interpretable feature space consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features. A forest-based model is developed for categorizing OHF posts. An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts. Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework. The past few years have witnessed the increasing popularity of online health forums (OHFs), such as WebMD Discussions and Patient, as communication platforms among patients. According to a survey by PwC in 2012, 54% of 1060 participants are comfortable with their doctors getting information related to their health conditions from online physician communities [https://patient.info/forums). We highlight the sentences about symptoms in orange, and the one about medication in violet. The former ones provide the information about the user's symptoms, reflected by the terms “heartburn,” “acid reflux,” “abdominal pain,” and “IBS.” The latter one tells the user's medication treatment, where the term “nexium” presents the medication for the disease. These pieces of information can help other users to gain a more comprehensive understanding of the disease.To extract insightful information from OHF posts, a commonly adopted strategy is to split posts into sentences and classify each sentence into different categories according to their semantical meanings , 3. For However, it is a challenging task to effectively analyze the expressions in online health forums. First, the user-generated content in OHFs is usually unstructured and contains background information that is relatively less important to analyze . The irrWe propose a forest-based framework to deal with the healthcare-related text classification problem. Labeled sequential pattern features are involved in characterizing the unstructured healthcare-related texts from both syntactic and semantic levels.We develop a method for constructing decision rules integrated from decision trees in forest-based models to achieve model interpretability.The effectiveness and interpretability of our framework are demonstrated through experiments on a real OHF dataset, where we analyze the interpretations provided by our framework in detail.In this paper, we propose an effective framework for analyzing OHF posts. We propose to develop a random forest model to classify the sentences into three categories, that is, medication, symptom, and background, in order to get an accurate understanding of the role of each sentence in the overall expression of the health situation. Besides, human-understandable interpretations for classification results are generated for the forest model. To enable interpretation, the features involved in the classification task are designed in a human-understandable manner. Moreover, the contribution of features to a classification instance can be explicitly measured by the decision rules constructed during training process –8. Specimedication, symptom, and background. The definition of each category is given as follows. Medication: If a sentence contains information relevant to curing diseases, treating any medical conditions, relieving any symptoms of diseases, or preventing any diseases, then we assign the sentence to the medication category.Symptom: If a sentence contains any contents relevant to departures from normal functioning or feelings of individuals, which may express the phenomenon affected by diseases, we assign the sentence to the symptom category.Background: If a sentence cannot be classified to the medication or symptom category, then we assign the sentence to background category.In this section, we will briefly introduce each module of our proposed framework  includinGiven a sentence “I am taking 90 units Lantus twice a day” for classification, for example, we will first convert it into an instance in a feature space through preprocessing to identify the number term “90,” the drug term “Lantus,” the frequency term “twice a day,” the context of each term, and so forth. Then, we will use the forest-based model to classify the sentence, along with the explanations based on the discriminative features identified by the model.medication, symptom, background}. Formally, let ℍ be the healthcare-related natural language space and 𝕃 = {medication, symptom, and background} be the target label space. Suppose a collection of N labeled sentencessi represents the original text of the ith sentence and li represents the label of the ith sentence. In other words, each sentence is labeled as medication, symptom, or background.In this module, we split the collected online health community posts into sentences and manually assign each sentence one label from the classes {f : ℍ  →  ℝD to convert healthcare-related sentences into instances in a D-dimensional numerical space, where D is the number of features used to represent each sentence. In this way, we can represent each unstructured sentence with a numerical vector, which facilitates model training and testing. After that, the overall dataset is transformed to𝒮 is the original labeled sentence dataset and N is the number of sentences, while xi =  is the resultant numerical instance represented by D features. These features are also intuitive and insightful to help people better understand the sentences. We will discuss this module in detail in In this module, we propose the feature extraction method F : ℝD  →  𝕃 that can classify an instance into a class from {medication, symptom, or background} and interpret the sentences belonging to class medication and symptom. We mainly introduce building forest-based models to classify and interpret the instances: (1) random forests [In this module, the task is to train a model  forests  are grow forests  is a metInterpretable features play an essential role in enabling users to understand prediction results. In this section, we discuss how to convert health-related sentences into instances in numerical feature space composed of labeled sequential patterns, UMLS semantic type features, sentence-based features, and heuristic features. The method of extracting labeled sequential patterns is introduced in detail.In sentence classification, if we simply use bag of words to represent each sentence, the overall data matrix will be huge and sparse, because there are a large number of terms, and many terms only occur in few sentences about some specific diseases. It is undesirable to use these raw terms to explain their correlations with sentence category as interpretations for classification results. The reason is that the raw terms do not explicitly specify the semantics of words, or contain the structural information of sentences. Therefore, we propose to use higher-level features to represent a sentence rather than words. We will rely on these higher-level features to interpret the sentences classification results.labeled sequences as preliminary representations of sentences [sequence → label, where sequence is a sequence of tags and label is the class label. To convert a sentence into a sequence, we use the tags in PRP, “I”), , , , , , ) and the entire sentence is represented as a labeled sequence:  → medication.We first extract entences . A label𝒮 = {,…, }, we convert each pair into a labeled sequence pi  →  li by applying the method mentioned above so that we can obtain the database 𝒟 of labeled sequences. Our next goal is to mine the frequent patterns in the labeled sequences from 𝒟 and adopt these frequent patterns as features to capture the characteristics of the healthcare-related sentences. This task can be divided into two steps: (1) frequent sequential pattern mining and (2) building frequent labeled sequential patterns.Given a training set of labeled sentences 𝒟. Before that, we first define sequential pattern as follows.We now focus on mining the frequent sequential patterns from database sequential pattern is a sequence of tags which is a subsequence of one or more sequences in the database. The adjacent tags are not necessarily adjacent in the original sequences, but their distance should be not greater than a threshold in the original sequences, which is set as 5 in experiments [A eriments .a, b, c, d, e, f)  →  l1 and   →  l2 in the database 𝒟,  can be considered as a sequential pattern of both sequences. Note that a sequence is different from a labeled sequence. The former only consists of the sequence of tags, while the latter includes the mapping from sequence to the label, that is, Pi  →  li.For example, given two labeled sequences  is a sequential pattern p′ with sup(p′) ≥ μ, where μ is a customized threshold and sup(p′) denotes the support of p′ in 𝒟, that is,A p is any sequence in the database 𝒟 that contains p′. sup(p′) represents the percentage of the sequences in the database that contain p′, which shows the generality of p′ in the database 𝒟.where 𝒟. The minimum threshold μ is customized by users such that the resultant FSPs would be general enough.There are several algorithms to mine frequent patterns from a database. We select CM-SPAM  to obtaiWith FSPs available, the next step is to select a subset of promising FSPs called frequent labeled sequential patterns (FLSPs) which are then used for classification.medication and symptom; thus, the FLSPs are different for each class. Formally, an FLSP of label l is defined as the FSP with high confidence with respect to l. Given a specific label l, the confidence of a frequent sequential pattern p′, denoted by conf (p′), is computed assequences that contain the FSP p′ and are labeled l to the sequences containing the FSP p′. FSPs with high confidence show strong relations to the given label l, since a large portion of those frequent sequential patterns are labeled as l.Note that we have two classes: support to 5%. Besides, the minimum confidence threshold might also not necessarily be set very large since we would like to obtain more FLSPs by reducing some predictive ability of them in the early stage. In the experiments, we set the minimum confidence to 85% [We would also like to set the minimum support threshold to a small percentage in order to include more FSPs. In our experiments, we set the minimum e to 85% .  Algorisequence contains a FLSP, we will set the value of the corresponding feature entry to 1; otherwise 0.Finally, we obtain a set of FLSPs, which can be used as features to identify the relationship between labels and patterns in sentences . We use https://mmtx.nlm.nih.gov/) [https://mmtx.nlm.nih.gov/Docs/SemanticTypes_2013AA.txt). Thus, for each semantic type feature, we set the value to 1 if the sentence contains any phrases related to the semantic type; otherwise, 0.In addition to FLSPs, we also use UMLS  Metathesih.gov/) , we can si in 𝒮, it is converted into xi which is a representation vector of the sentence in the feature space of FLSPs and the UMLS semantic types. If si contains any FLSPs or phrases related to UMLS semantic types, the value of the corresponding feature entry in xi is set to 1.Generally, for each sentence Sentence-based features are capable of representing the sentence in a direct way . In thisAlthough word-based features such as bag-of-word representation usually suffer from the curse of dimensionality, we still take them into account to compare the classification performance because of their effectiveness . UnigramCapitalized words and abbreviations can be good indicators of whether there are any medical terminologies in the sentence, which could be highly related to medication or symptom sentences. We can use two binary features to indicate whether the sentence contains any capitalized words or abbreviations, respectively.In addition to all the features originated from the texts of the sentences, we can also adopt useful side information of posts . SpecifiIn general, we can select different combinations of the features introduced in this section to represent health-related sentences and then build models to predict the categories of sentences with interpretations.In this section, we first introduce the classification of health forum sentences using a random forest model and how to interpret the forest model with features of high importance. Second, we introduce how to collect rules from decision trees in the forest to construct a new pattern space  and achiNT instances at random with replacement from the training set. The samples will then be used to grow the tree model.Sample m features are selected from the total D features at random, where m ≪ D. The best split on the m features will be used to construct the tree nodes such that the Gini impurity for the descendants will be less than that of the parent node, using the method introduced in CART [m remains constant during the forest growing process.A subset of  in CART . The valEach tree grows to the maximum size without pruning.A random forest consists of an ensemble of tree-based classifiers and calculates the votes from the trees for the most popular class in classification problems . The groWhen growing a tree using the samples from the original training set, about one-third of the instances in the training set are left out of the samples selected at random. This out-of-bag data will be an unbiased estimate of the classification accuracy for the currently growing tree and also can be used to estimate features importance.The classification mechanism of a random forest is explained through a set of decision paths. To interpret random forest models, we propose to quantify the contributions of node features, rank them according to their contributions, and find out the most discriminative ones , 18.M is the number of leaf nodes in the tree. cm denotes the criterion score, which is a scalar in regression problems or a vector in classification problems, learned from the training process. x is the input sample. Rm is the path from the root to the mth leaf node. I is an indicator function identifying whether x is run through Rm. As we are solving a classification problem, cm and f(x) should be vectors whose sizes are the number of the classes. The ith value in the vector f(x) represents the criterion score of the instance x being classified into the ith class, which can be converted to a probability value by normalization. In our problem of classification, an input instance x is classified into one class from the classes {medication, symptom, background} according to the maximum probability specified by f(x) of the decision tree.For a decision tree in the random forest, its decision function can be formulated as follows:criterion score  vector by calculating the score vector difference between the current node and the next node in the path. The final prediction result along a tree path is determined under the cumulative influences of nodes in the path. Therefore, a prediction can be defined as a sum of feature contributions plus a bias:FCt,k(x) is the feature contribution vector from the kth feature in the tth tree for an input vector x, D is the number of features, and βt is the bias of tree t. Both FCt,k(x) and βt are criterion score vectors. Our goal is to calculate the feature contributions for an instance x classified by a decision tree t that has been trained on the training set. Specifically, it is achieved by running through the decision paths in tree t. On the root node in the path, FCt,k(x) = 0 and ft(x) is initialized to βt. Each time the instance arrives at a node with a decision branch on the rth feature, and FCt,r(x) will be incremented by the difference between the criterion scores at the child node along the path and the current node. Once the decision process of x reaches a leaf node, we assign a class to x and obtain all feature contributions along the decision path.From another perspective, we can observe how a feature contributes to the T is the number of trees in the forest. Similarly, the prediction function of a forest can also be decomposed with respect to feature contributions:FCt,k is the contribution of the kth feature in the tth tree. Therefore, the contribution of the kth feature to classify an instance x can be defined asmedication or symptom category, is to find out those features with the most contribution to leading an instance to medication or symptom leaf nodes. We will not interpret background sentences since they are not as informative as the other two classes.The prediction function of a forest, which is an ensemble of decision trees, takes the average of the predictions of its trees:F(x) is constructed given the training set 𝒳 = { | 1 ≤ i ≤ N} with N labeled instances. To find out the important features for category medication and symptom, we select two subsets of training sets whose labels are medication and symptom, respectively. Let 𝒳M = { |  ∈ 𝒳, l = medication} be the subset of medication instances and 𝒳S = { |  ∈ 𝒳, l = symptom} be the subset of symptom instances; the average feature contributions for the two classes can be calculated as follows:kth feature for medication class and symptom class, respectively. After computing the contribution of features for each class, we rank these features to indicate their relative significance. Finally, the ones with larger contribution are selected as the discriminative features of each class.Suppose a random forest model To further exploit interpretability, we extract decision rules from the forest model to form a new space, where the forward selection is applied to select the top discriminative decision rule combinations, that is, discriminative patterns .pattern is defined as the form ofxi,j is the value on feature j of instance xi and vj is a scalar threshold. In our problem, a pattern can be any combination of rules from a decision tree. Furthermore, discriminative patterns (DPs) are those strong signaling patterns with high information gain or low Gini impurity in classification. In our problem, a pattern refers to a complete decision path, and a discriminative pattern is the path with low Gini impurity.Specifically, a K discriminative patterns. Let fs be the forward selection function, then we have fs : {0, 1}|DP|  →  {0, 1}K. We run K iterations, where the DP set at iteration k is denoted as Patk. At iteration I, we traverse the discriminative patterns dpj ∉ PatI−1. A temporary DP set PatIj at current iteration is built by adding dpj to the DP set obtained in iteration I − 1, that is,However, since the dimension |DP| of discriminative patterns is still high, we need to identify the most informative ones from them. To this end, we apply forward selection  to selecIj and obtain the accuracy accIj of the classifier. The best pattern dpj∗ is added into the DP set, where j∗ = arg maxjPatIj and accIj∗ > accI−1, so that PatI = PatIj∗. After K iterations, we obtain the top K discriminative patterns PatK. At last, each instance x in the dataset is mapped to the DP space as y ∈ {0, 1}K. If the kth pattern appears in x, then the corresponding entry yk is set to 1; otherwise, 0.Then, we build a classifier using support vector machines  based onIn this section, first we present the experiments results which show that the forest-based models outperform the baseline methods. Second, we compare the interpretability between Lasso and our forest-based model by analyzing their discriminative features and discriminative patterns.http://patient.info/forums) using Scrapy, a python framework. The ground truth was obtained by assigning a label to each sentence in the data set. 257,187 discussions in 616 subforums from the forum were crawled. Then, we used NLTK tokenize package (http://www.nltk.org/api/nltk.tokenize.html) to split the texts in each discussion into a list of sentences. Given lists of sentences from all the discussions, we randomly select sentences from each list in portion and the number of selected sentences is 2585. We recruited two volunteers to complete the labeling work. Both volunteers were provided with the total 2585 randomly selected sentences and asked to categorize each of the sentences into medication, symptom, or others. The labeled sentences were merged based on unanimous voting. We discarded the sentences that were labeled with disagreements and obtained 2099 sentences categorized into the same label. The result of the sentences labeling is in background, medication, and symptom to 0, 1, and 2, respectively.Since there are few datasets available for health-related texts classification, we created our dataset by collecting texts from online health communities to solve this problem. The data used for the experiment in this study were crawled from patient.info (medication and symptom with RBF kernel exp(−γ|x − x′|2), where γ is the reciprocal of the number of features. To predict an instance, the SVM models calculate the probabilities using Platt scaling. If the probabilities to classify the instance into medication and symptom are both less than 0.5, then we classify the instance into class background; otherwise, it is classified into the class with greater probability. In order to ensure the performance, we implement feature selection based on entropy using a decision tree model. In terms of the second contribution, we compare the model interpretability between Lasso [The contributions of our study we want to claim are how much improvement of the performance our proposed method can achieve by introducing the labeled sequential patterns as features and how the interpretability can be enabled by applying our proposed methods to sentence representatives in a variety of spaces to gain an insight of the health-related text classification model. To show the first contribution, we choose support vector machines trained on a variety of features proposed in . We builen Lasso  to randoF1 score. For multiclass classification, the weighted average precision, recall, and F1 score can be computed as follows:N is the size of the test set, L is the label set, that is, L = {medication, symptom, background}, Nl is the size of the test subset with label l, and precisionl, recalll, and Fl1 are the precision, the recall, and the F1 score of the binary classification for instances with label l.The metrics for the evaluation are accuracy, weighted average precision, weighted average recall, and weighted average For the SVM model, the entire average predicting accuracy achieves 79.8% with only word-based features, which outperforms the accuracies of Lasso. SVM also performs very well in terms of precision, recall, and F1 score. The model trained on LSP features alone fails to outperform the model trained on word-based features, but the former could achieve better performance than the latter if we add the UMLS semantic type features. Note that there are only hundreds of LSP features while there are more than 16 k word-based ones. Without feature selection, the performance of SVM is not very good, since the word-based features are considerably sparse. Furthermore, SVMs with RBF kernels do not provide interpretability directly for us to gain an insight of the sentences although the models achieve good performance.From the experiment results using Lasso, we can find that the recall scores for classifying medication sentences are better than those for symptom ones, while the accuracies and precision scores indicate the opposite trend. As we use multiclass classifiers, many of the test instances are classified as medication class. The Lasso models trained on the word-based features slightly outperform the ones trained on the LSP features. As For the forest-based model, we can find that the accuracies of medication and symptom class can both achieve more than 80% with only LSP features and UMLS semantic type features. The overall accuracy achieves 80.9% and outperforms the other methods. Besides, with LSP and UMLS semantic type features, the precisions and recalls of both classes are greater than 0.8. Moreover, with position feature and word-based features, the performance of the forest-based model is even better. In general, the random forest model can achieve the relatively better F1 scores for both medication and symptom sentences classification. Similarly, the random forest models trained on the word-based features slightly outperform those trained on the LSP features.Although it is not guaranteed that the models trained on LSP features outperform the ones trained on word-based features, we would still like to take advantage of LSP features since the feature dimension is significantly reduced without sacrificing the discrimination ability of models. In addition, LSP features provide a valuable perspective in both tag and structural levels to interpret classification results for health-related sentences., for example, is common for symptom-related sentences like “someone suffers from some symptom frequently/occasionally.” However, we also find that the tag SYMP is very frequent in both medication and symptom sentences, which is due to the reason that Lasso could not achieve good performance using LSP features, and is also hard to interpret the differences between class medication and class symptom.For LSPs, they are usually assigned with positive weights as they are capable of mining the symptom terms in the sentences. The pattern Several UMLS semantic type features are assigned relatively larger weights to identify symptom sentences. For example, the term “sosy,” short for “sign or symptom,” is obviously a useful feature to identify symptom sentences. The term “mobd”  can be used to detect mental disease symptoms. “patf”  is a parent semantic type of “mobd,” which is also an informative feature to detect pathologic terms.To interpret healthcare-related sentences in forest-based models, we calculate the feature contributions from decision trees in the forest. We select one random forest model with the best accuracies in the experiments and list the 10 features with the greatest contributions for each class in medication sentences, the unigram feature “prescribed” has the largest contribution. This is because such kinds of sentences usually contain information about prescribing drugs. LSP features , , , and  also contribute to recognizing sentences as medication-related ones as they all contain the POS tag CD, which represents the numbers in describing the dosages of medications. The morphological features are selected as the names of many drugs are capitalized terms or abbreviations. The UMLS semantic type feature “hlca”  is important since healthcare activity terms are commonly seen in medication sentences. On the contrary, if a sentence does not contain LSP  or “sosy” (“sign or symptom”), or is not posted by the user (thr. Crt. = 0), this sentence may also be classified into medication class, as it is less likely to be symptom-related.In identifying symptom class, the UMLS semantic type features “sosy” and “patf” are among the top relevant ones since they are capable of detecting symptom terms and pathologic terms, respectively. Thread creator indicator is also useful since symptom sentences are mainly posted by users to share their situations and ask for more information. If a sentence does not contain the word “prescribed,” then it is less likely to be medication-related. LSP features , , and  are selected since there are usually multiple terms matching the tag SYMP in symptom sentences. The position feature is also important in identifying symptom class, as it is natural for users to mention their symptoms in the first vth1 posts, where vth1 is a threshold learned by the decision tree. Similarly, if the number of words from a sentence is greater than vth2 learned by the decision tree, the sentence will be more likely to be a symptom sentence.For the Compared to the feature ranking in Lasso, we can have a better understanding from the feature contribution rankings for each class in the random forest. The relationships between features and classes can be learned from the feature contribution vectors while Lasso only provides the weights of the features, which may not be expressive enough to represent the relationships between features and classes. The random forest model can achieve both better performance and interpretability compared to Lasso.K = 30 to obtain the top 30 DPs. SYMP are likely to occur in symptom sentences, while tags CD and DRUG often lead to nonsymptom leaves as they are more likely to occur in medication sentences. In another word, symptom sentences usually contain symptom terms while medication sentences usually contain drug terms and numbers which represent the dosages of the medications. In addition to LSP features, there are two conspicuous unigram patterns “anxiety” and “cough,” because the training set contains many sentences related to anxiety and cough conditions.DPClass  proposesPrevious medication information extraction research mainly focused on extracting medication information from clinical notes, such as  using coOur work focuses on both classifying and interpreting the online healthcare-related texts. The major challenges in healthcare-related text classification and interpretation are how to represent the texts and how to classify and interpret the data. For the former question,  proposesIn our research, we propose to use labeled sequential patterns to represent the healthcare-related sentences in order to reduce the dimension and sparsity of the data, which can both guarantee the performance and enhance the efficiency. Then, we build forest-based models on the training data which is capable of predicting with decent performance and interpreting the healthcare-related sentences by extracting the important features used in the decision rules, ranked by their contributions, and the discriminative patterns consist of the decision rules. Overall, the forest-based models trained on the proposed feature space can achieve good performance and enable the interpretability of the data. In the future, we will build a compact system based on this framework to help users directly extract and highlight the insightful sentences while they are viewing healthcare-related articles, posts, and so forth Moreover, we will also target to extract and interpret the insightful sentences from other categories such as medication effects and user questions and include data from other sources like clinical notes.


Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge.a posteriori probabilities. The MCMC-based method uses the probabilistic model DLTRS, that integrates LGT, gene duplication, gene loss, and sequence evolution under a relaxed molecular clock for substitution rates. We can estimate posterior distributions on gene trees and, in contrast to previous work, the actual placement of potential LGT, which can be used to, e.g., identify “highways” of LGT.In this paper, we propose probabilistic methods that samples reconciliations of the gene tree with a dated species tree and computes maximum Based on a simulation study, we conclude that the method is able to infer the true LGT events on gene tree and reconcile it to the correct edges on the species tree in most cases. Applied to two biological datasets, containing gene families from Cyanobacteria and Molicutes, we find potential LGTs highways that corroborate other studies as well as previously undetected examples.The online version of this article (doi:10.1186/s12859-016-1268-2) contains supplementary material, which is available to authorized users. Lateral gene transfer (LGT), also known as horizontal gene transfer, is the transfer of a gene from one organism to another organism such that both organisms live at the same time. LGT can be mediated by viruses, plasmids, and transposons, and is common in bacteria and archaea . It is aAmong bacterial genomes, LGT is often observed between closely related species as well as distantly related species . The keySince LGTs are also observed between distant bacterial species, they are a confounding factor in inference of phylogenetic trees. Inference of gene phylogenies inside a species tree in the presence of LGT is therefore not a trivial task. A number of methods have been proposed to solve the gene-species reconciliation problem in this context. Goodman et al.  introducDLTRS , introduced by Sjöstrand et al. , 14, is Another attempt to model LGT, in the context of gene-species tree reconciliation, was made by Suchard . A hierax,y〉 in the species tree, each gene lineage is exposed to gene duplications (GD), gene losses (GL), and LGTs at rates δ, μ, and τ, respectively. When a gene lineage u is exposed to a GD event, it is replaced by two children, which both continue evolving over the same species tree edge as did u. When the gene lineage u is exposed to an LGT, it is replaced by two children: one continuing to evolve over the same species tree edge 〈x,y〉 as did u, and one evolving independently over another species tree edge, chosen uniformly from those concurrent with 〈x,y〉 at the time of the LGT event. A loss of the gene lineage u removes it from the process as well as from the generated tree, in which its former parent is suppressed. Each lineage reaching a speciation vertex y in S splits into two independent processes, each evolving down a distinct outgoing edge of y. The process continues recursively down to the leaves where it stops. So, a gene tree vertex represents either a speciation, a GD, or an LGT event; the divergence time for a speciation vertex is given by the corresponding species tree vertex, while the divergence time for a GD or an LGT vertex is given by the DLT process. Divergence times associated with vertices of a tree induce edge times as well as time intervals, in the natural way. The DLT-model also generates a realization explaining how the gene tree has evolved by mapping each gene tree vertex to where in the species tree it was created, i.e., a vertex of the species tree or a species tree edge combined with a time point along it.Over any edge 〈via a relaxed molecular clock, effectively transforming dated trees with leaves representing extant entities, such trees being necessarily ultra-metric, into trees consistent with a relaxed molecular clock. This provides a biologically realistic prior distribution for edge lengths—the convolution of edge times and substitution rates conventionally used in substitution models. In our implementation, edge substitution rates are independently and identically gamma distributed.The substitution rate model obtains biological realism Finally, sequence evolution over the gene tree, with these edge lengths, can be modeled using any of the standard substitution models used in phylogenetics .In this section we describe the core of our method, but defer many details to the Additional file D and a dated species tree S. For computational reasons, the species tree S is discretized  to summarize all model parameters. All rate parameters can be specified as input, or be inferred during MCMC.Sequence evolution is modeled using standard substitution models. The edge rate model is a Gamma distribution with parameters G and a species tree S. Gene tree vertices are mapped to a vertex or an edge in the species tree in a reconciliation. A realization, maps vertices of a gene tree to vertices of a discretized species tree S″. Reconciliations and realizations map the gene tree vertices in a manner consistent with the gene tree; a gene tree vertex is never mapped closer to the root in the species tree than its parent. In addition, a realization never maps a child vertex and its parent to the same time. We also define continuous realization as a reconciliation where each gene tree vertex mapped to a species tree edge is associated with a time.  and p(.), respectively. The first factor P is computed by the standard so-called peeling algorithm [p was the main algorithmic contribution in [p(θ) is assumed to be uniform and independent. The denominator P(D|S), the normalizing constant, is not calculated when using MCMC because it cancels when computing acceptance probabilities.The DLTRS model is applied in a Bayesian MCMC framework to estimate a posterior distribution over gene trees with edge lengths, and other parameters of the DLTRS model. This framework performs an algorithmic Rao-Blackwellisation , 21 overlgorithm . An algoution in , which ip  is then approximated as following: In each iteration of the MCMC, a combination of ordinary differential equations (ODE) and dynamic programming is used to compute the factor ,S)  and D(c) are the sets of continuous realizations and realizations, respectively, compatible with the reconciliation c. The factor Δ(d) is the product of the lengths of the intervals in which the discretization points used by d are found, and accounts for that we are approximating integrals over these intervals.where p can be reused for inferring reconciliations and realizations, both for sampling and maximum a posteriori (MAP) estimation. The sampling is performed by, in preorder over the vertices of the gene tree G, sampling discretization vertices V(S″) to map the gene tree vertices to. That is, for each internal vertex u of the gene tree, i.e., V(G)∖L(G), a vertex x in S″ that u is mapped to, is sampled conditioned by where the parent of u is mapped and how the process continued from there. That u is mapped to x, will be denoted ’ u→x’. We will also determine the type of event that a gene tree vertex u mapped to x corresponds to and denote this ’ u→x,speciation’, ’ u→x,transfer’ or ’ u→x,duplication’, with the natural interpretation. MAP estimation is performed using dynamic programming, by adapting the method for computing p. For details, please see Additional file The datastructures used to compute d and d′) of a gene tree G, in order to compare true realizations and the inferred realizations in simulations. The topological distanceG. A gene tree might have more than one transfer event and we therefore consider both the average topological distance and the maximum of the topological distance between the transfer events of the two corresponding realizations. Let q be a posterior distribution q over realizations of MAP gene trees (obtained in the MCMC framework). For every d′ from q, we get an average topological distance q, are obtained and are represented as We want to quantify the difference between two realizations , measuring how far away from the true LGT event the estimated posterior is. Figure mentclass2pt{minimWe applied our method to the two biological datasets studied by Sjöstrand et al. : MollicuBased on the posterior probabilities of LGT, we estimate a total of 266 expected transfers in the Mollicutes dataset, so on average about one LGT in every third gene family, and we have 122 predicted LGT events with posterior probability higher than 0.5. Similarly, in Cyanobacteria, the total expected number of transfers in MAP samples was estimated to 575, i.e., about one LGT in every fourth gene family. We get 94 LGT events predicted with probability higher then 0.5.Mesoplasma florum L1 and the ancestoral copy of Mycoplasma capricolum subsp. capricolum ATCC 27343 and Mycoplasma mycoides subsp. mycoides SC PG1 appeared (with posterior probability higher than 0.5) in 116 gene families  would be informative, especially since there are scenarios inferred by DLTRS that might be better to interpret as ILS.Our simulation results show that the DLTRS-sampler performs well in terms of identifying gene-tree edges corresponding to LGT events. In addition, it often also correctly identifies the species tree edges between which LGT events have occured, i.e., both the species lineage that the gene is transfered from and the one it is transfered to. This behaviour suggests that it can provide an accurate method for identifying highways of LGT. In fact, we used these from and to lineages information in our biological datasets analysis and detected some of the interesting LGT highways that are reported by others , 30, 31.


Family carers manage a wide range of responsibilities in the lives and care of patients receiving palliative care. They fulfil multiple roles and perform activities within different settings. This has immediate consequences on family carers’ every-day lives. According to literature, family carers in palliative care are both part of the formal and informal care network, but also persons in need of support. This article aims to investigate 1) burdens and rewards associated with family caregiving and 2) what family carers find helpful in their contact with professionals from integrated palliative care initiatives (IPC-i) and other services.Family carers looking after patients with cancer, chronic obstructive pulmonary disease or chronic heart failure were purposefully recruited at 22 IPC-i in Belgium, Germany, Hungary, the Netherlands and the United Kingdom in the course of the project “Patient-centred palliative care pathways in advanced cancer and chronic disease” (InSup-C).n = 156) and 87 quantitative questionnaires  were conducted with family carers. Interviews were analysed with transnationally agreed thematic codes (MAXQDA or NVivo). Statistical tests (SPSS) were carried out in accordance with the characteristic value of the items and distributions.Semi-structured interviews . We recommend recognizing family carers as part of the ‘unit of care’ and partner in caregiving, to improve their knowledge about, and access to, and the support available. Most people with non-curable life-limiting disease want to remain at home as long as possible –3. In soNational and international level information about family carer backgrounds –10, theiThe overall physical and psychosocial strains and deterioration of carers’ QOL are well recognised in the literature . PhysicaIn the European healthcare sector, palliative care was established to meet the needs of terminally ill patients. Across Europe there are different models of palliative care, and types of palliative care services to support family carers looking after patients that wish to die at home . Patient“Integrated palliative care involves bringing together administrative, organisational, clinical and service aspects in order to realise continuity of care between all actors involved in the care network of patients receiving palliative care. It aims to achieve quality of life and a well-supported dying process for the patient and the family in collaboration with all the caregivers, paid and unpaid.” The comprehensive approach of palliative care aims to maintain the best possible well-being of patients and their family carers. For this purpose, IPC goes beyond the scope of standard healthcare where healthcare professionals (HCPs) act as single entities in an uncoordinated and multifaceted care situation. An integrated person-focused palliative care approach for the patient and his/her social environment care “is a ‘complex intervention’ where management and organizational processes to support integrated care occur at many levelsHowever, as set out in the protocol article of the InSup-C project, initiatives using an integrated palliative care (IPC-i) approach show promising results in how they reduce fragmentation and enhance continuity of care as well as quality of care. Within the InSup-C project, IPC-i were differentiated from other PC initiatives and defined by being an established local and multidisciplinary PC collaboration with at least two different organizations and their HCPs providing direct patient care. The initiative was either targeting one or more of the following diagnostic groups: advanced cancer, COPD, CHF .Since, within the literature, it is largely unknown how family carers experience integrated palliative care, with this paper we aim to investigate 1) the burdens and rewards associated with family caregiving within IPC networks, and 2) the effects of IPC-i support systems and interventions for family carers.This study is part of a multiple embedded case study, conducted by the InSup-C project consortium. This study has a mixed-method design . Mixed mIn five European countries, IPC-i were selected based on a standardised selection criteria such as locally established networks, collaboration structures and multidisciplinary teams . After tFurthermore, though family carers are mainly female and kin, such as  partners and children , 30, theFamily carers were asked to complete questionnaires at the initial contact (baseline) and each month over a period of 3 months (follow-up interviews). In addition to items on demographic and epidemiological data, three standardized questionnaires were used. The Palliative Care Outcome Scale (POS) – version 1 measured patients’ quality of life (QOL) and perceived symptoms from the family carers’ perspectives . The CarThe international InSup-C project team developed an interview guide before starting the interviews, which was piloted by two project members and two family carers per country. In the interview, questions regarding patients’ problems and needs and family carers’ experiences of contacts and collaboration with and between professionals were discussed. To visualize the care network a card technique supported the family carers in describing complex experiences. For the interviewee, the researcher wrote down all persons, who were involved in the care network on cards, which then were used for further sorting and exploration of entities within the care network . InterviMissing values: Item-non-response technique was a replacement by mean scores of the other items when a minimum of 40% of items in the CRA subscales or POS summary scores had been completed. Otherwise these mean scores were excluded. CANHELP Lite questionnaire’ subscales were excluded if more than half of the responses for that domain were missing .Using SPSS , in dependence of scale level and distribution different statistical test procedures were applied . The variable ‘relation between patient and family carer’ was re-coded from four to three values.p-value was less than or equal to 0.05.We investigated correlations between family carers’ burden and demographics, patients’ QOL (with the POS summary score) and family carers’ experiences with care (with the five CANHELP Lite questions). Effect sizes were determined according to guidelines of behavioural sciences; a correlation coefficient of 0.1 was considered small, a correlation of 0.3 as medium or moderate power and an absolute value of 0.5 as large . We rejeAnalysis consisted of two major parts. The first part was a general national analysis in which every country analysed the interview data by performing content analysis in several steps. First, main themes about the organizational structure of and relationship within the PC networks, patients’ and family carers’ needs, problems and solutions were deductively applied to several interview transcripts. These themes were derived from the interview protocol and covered the broad scope of the InSup-C project . Second,The second major step was an in-depth analysis of family carer data. The main researchers GA and AE asked the international team to analyse their country-specific data by focusing on family carers’ tasks, burden, rewards, and collaboration with IPC-i by making use of the relevant codes from the codebook and by providing interview quotes. Furthermore, team members were asked for an overall impression of the family carers’ situation and general background on facilities for family carers in the countries. Answers had to be returned in English. GA and AE interpreted, compared and analysed the national data, verified their results with the international team, and circulated a first draft article to assure right interpretation of the data and thus, rigour of the findings. Findings of this study are explorative and descriptive, since the scope and design of the greater InSup-C project were very broad . Illustrn = 156 interviews). Almost half  of the family carers stopped prematurely either because they signed out or the patient died. After data cleaning procedures among the 87 respondents 27% (n = 23) only finished the baseline questionnaires, 14% finished both baseline and month 1 (n = 12), 6% (n = 5) participated till month 2 and 54% (n = 47) completed the entire period. Family carers’ characteristics and some basic data about the patients they cared for are presented in Table For the qualitative part, 92 semi-structured baseline, 50 secondary and 14 bereavement interviews were available .” .“I have cardiac arrhythmias and partially shortness of breath; I am not the healthiest one and I am truly trying to stay afloat (…).” .“I have pain in my back, always pain in my legs. I have had shingles and I am taking pills against anxiety, to calm me down.” .In all IPC initiatives family carers stated a range of current health issues at the time of the qualitative interviews, especially older family carers who listed pain , cardiac dysrhythmia, insomnia, shortness of breath, restlessness and feelings of guilt and anxiety as forms of physical and mental distress.“I can‘t lift her anymore. In the morning, the children are gone, so that I am already in the morning in troubles. (…) just as an example I can‘t do this anymore.” .As a consequence of concurrent responsibilities, control over their own health and needs was often suppressed to provide patients with the best possible quality of care at home. Some of the older family carers questioned whether their own health conditions would allow them to continue performing caregiving activities. The overall tenor was that a further worsening of own health would lead to decreased caregiving ability at home.“I was in a different world really. I’d been caring for him for so long that I think my… my own self had kind of gone into the background … I’ve lost a lot of weight because I suppose I haven’t been looking after myself at all. I mean I’m not aware that I’m not looking after myself but obviously it’s taken its toll, which is bound to happen.” .Looking at family carers’ own conditions, one family carer reflected on how the ongoing process with the patient explicitly affected her personal dietary needs, which resulted in a distinct loss of weight:I: What are your needs?C: Rest, mentally and physically. I am never at ease, not when I am with him and not when I am without him.I: Do you ever go out?C: Never .In the course of the interviews, topics of psychological burden and social isolation caused by caregiving responsibilities were mentioned very often. Most of the family carers yearned for additional free time or respite care.“I do not think caring for my husband is very hard, well, it is difficult to see him deteriorate, but I don’t mind caring for him. Lots of people tell me: ‘Remain strong!’ and yes, the fact that I can do a lot for him keeps me strong. Probably I will get problems when he is not here anymore to care for.” .Qualitative findings did not extensively describe rewards. A family carer stated that he learned a lot about himself in the process. Other quotes were about experiences of connectedness between patient and family carer. Family carers related this to special moments, when they felt gratitude or happiness that the person cared for was still alive.“That was so important for me. He died in the home where he was born.” .In bereavement interviews some family carers spoke modestly or even with humility of what they have achieved:“I am helping as much as possible with everything she needs. And when she asks me, I say to her “honestly, I do so with love”. I wouldn’t want it any other way, although it is tough.” .Furthermore, throughout the interviews a number of motives for caregiving became evident. It seemed that family carers tended to not focus on their own interests and well-being, finding ‘the other’ more important and considering informal care a ‘duty’. In some cases this was a result of reciprocal action, meaning that the family carer received help from the patient before they fell ill. In other cases, caring was considered as only natural. In a few cases caregiving was seen as a heavy burden which was not always taken on willingly, particularly when there were no other solutions for the provision of care to be seen. Most of the time family carers – for diverse reasons – tried to cope and care on their own as long as possible. This was especially mentioned by family carers living with the patient, most of whom were spouses. There was also the motive of love, which in some cases buffered or reprioritized the negative aspects of caring.“Those nurses make me feel at ease. (...) in the beginning you have to get used to the different faces, but now we have the same ones during the week and it changes a little at the weekends. They are five in total, no, they don’t bring in new staff, so we got used to them all.” .“We have a good relationship with the doctors. My mom always got answers to her questions. If she needed any treatment, her doctor informed her about the treatment and the possible side effects. If I perceive anything unusual, I can ask the doctor.” .In the qualitative results family carers who were frequently in contact with an IPC-i team stated high satisfaction with assistance. Many quotes indicated that an overall gratitude towards IPC-i is due to a pro-active support for patients. In conjunction with this we received a lot of positive comments in all countries. Thus, IPC-i involved in this study gained a lot of trust: those who have a contact person for every upcoming situation 24/7 gave them a feeling of confidence in dealing with the home care situation.However, qualitative findings showed that there were some flaws in access to information, personnel and services in all countries. These concerned: preparedness for transitions between care settings, continuity of personnel, timely and pro-active information and mobilisation of services, lack of access to specific care if this was not covered for the specific disease (non-cancer mostly) and responsiveness of HCPs.“There are a lot of things that can be arranged for medical care, but you have to arrange everything yourself. They tell you or they don’t, and you always have to do it yourself.” .Within our data most of the IPC-i showed limitations in recognising family carers’ needs and did not seem to provide pro-active support for family carers .“And then he (IPC physician) gave me this brochure about the last weeks and days. (...) And that was very helpful and I have given it to our visitors to read it.  doctor said to my husband, you don’t have to eat. If you like to eat, it’s fine, but you don’t have to eat. Usually, you don’t hear that. You always hear how people repeatedly say you still have to eat (...).” .Getting appropriate information about care in the last days of life can help to tackle care conditions that had caused feelings of uncertainty and helplessness at this sensitive time. A female respondent explained how receiving information about diet and nutrition in palliative care from an IPC physician was seen as an essential component to reduce the carer’s stress and lack of self-confidence.“I was like, I would have appreciated if they told earlier. Then we could have adapted our reactions to him. […] He just knew. If we would have known earlier, we could have handled the situation differently.” .Furthermore, most family carers within an IPC network expressed their need for information to overcome their uncertainties after discharge from hospital or in the last days in life:Qualitative findings showed that HCPs from IPC-i provided practical and some emotional support to reduce family carers’ responsibilities. Practical and medical interventions for the care of a patient were offered most frequently: cleaning, medical home care or instructions about easy nursing tasks by either the initiative or the general practitioner. Emotional support from HCPs, however, appeared to be more ambiguous. Many family carers reported that HCPs were mostly supportive in their approach: being attentive and genuine, taking time and creating a trusting environment. Feeling involved and listened to in matters of care and decision-making was considered helpful by family carers, but was not an experience shared by all. During the interviews, offers of specialised psychological or spiritual help for family carers were reported only rarely.“We did have Hospice coming… when he was seriously ill, we had Hospice coming in at nights for two nights a week and then, as he got better, there wasn’t a need. And they came out and they assessed him and they said we’ll still send someone on a Friday so that you can shop which, you know, is good.” .Respite care as a form of support was only offered in a few cases. Especially one case, however, showed that it released family carers from stressors and strains at a personal level:“(I): And then you said they were doing something for you personally now, what’s that? (FC): Yes. It’s complementary therapy, it’s like aromatherapy, massage. (…) It’s lovely, yeah, it’s really nice.” .Family carers who participated in this study mentioned the usefulness of practical and psychological support provided by IPC services. An example showed how an acknowledgment of the family carer’s role in the care network improved the carer’s overall situation:Active offers to support the well-being of family carers were provided ad hoc rather than as systematic interventions following ‘care pathways for family carers’ including needs assessment on a regular basis developed or used by IPC-i.The aim of this paper was to analyse the family carers’ situation within an IPC network in five European countries . Our quantitative data showed moderate burden with a high positive impact on family carers’ self-esteem, but the qualitative data underlined a higher risk of physical and psychological health issues as well as an actual deterioration of health, especially in older or spousal carers. This higher risk on (over)burden among spousal or older carers in general and when patients’ QOL decreases is consistent with other results in this field , 41, 42.The differing quantitative and qualitative findings on family carers’ burden can be interpreted as a consequence of change in family carers’ perceptions. They might have focused on the patient rather than on changes within oneself and might have compared their own health issues with that of the patient. Furthermore the motive of love for caring, might have buffered or reprioritized negative aspects of caring, as has been highlighted in other studies –45.In the literature several motives for caring are seen as potential risks for burdening, e.g. personal bond between patient and family carer , desire From this study, it appeared that HCPs from both IPC-i and from outside IPC-i provided some practical and emotional support to family carers to release their responsibilities. Our quantitative and qualitative findings confirmed that reliable and consistent transmission of information and regular contacts with IPC-i were important to lighten feelings of uncertainty. The same conditions more or less accounted for access to services and personnel. Literature also made notice of these conditions, but most often as things that are lacking in practice –53. WithAs was shown in our results and in literature support at home is reported to be the most common intervention to support family carers , 55. QuaEven though IPC-i aim at taking care of both patients and family carers, the barriers to offer pro-active care for family carers seem to lie within the healthcare systems. The allocation of help still seems to depend mostly on whether or not family carers clearly express their need for support. Active interventions on an operative level by IPC-i, such as the two well illustrated examples in this study, are singular events even within an advanced healthcare system. In view of our results, HCPs of ICP-i need to recognise the gradual reduction of carers’ welfare in a timelier manner and consider patients and family carer as part of the ‘unit of care’. Ongoing pro-active carer support including respite care, individualised training courses, counselling services and assessing their healthcare condition on regular basis may help to strengthen and empower family carers for their caregiving duties in advance . HCPs clThe study’s sample size as well as its international character increases the transferability of the findings. However, our study also has some limitations. Since the scope of the research project InSup-C was very broad, this study was mostly explorative and hypothesis-generating. Findings of this study as well as suggestions for clinical practice need to be tested and investigated by further research. Since we used selected IPC-i for the gathering of data, findings cannot be generalized for all countries. Another limitation is that quantitative statements about satisfaction with care were based on individual questions of the CANHELP Lite. To resolve this, these statistical results were complemented by qualitative findings. Finally, it might be that, during interviewing, researchers unintentionally focused on burden of care. This would explain the discrepancies between qualitative and quantitative results, but would indicate a data bias. Focusing on the negative side of caregiving is a trend within literature as well , 61.Needs of family carers were similar in all participating countries who on average experienced a moderate burden from caregiving, which seems however underrated in the light of the findings of the qualitative analysis. Consistent communication towards family carers, in particular information sharing, came afore as being inversely related to carers’ burden, stressing the importance of communication in end of life care. Qualitative findings indicate a need for proactive care for family carers, for example provision of respite care, training, and access to resources. This study recommends recognising family carers as part of the ‘unit of care’ and partners in caregiving at the same time, to improve their access to support and their knowledge about available support options.


Using mixed design analysis of variance (ANOVA), this paper investigates the effects of a subtle simulated increase in adiposity on women’s employment chances in the service sector. Employing a unique simulation of altering individuals’ BMIs and the literature on “aesthetic labour”, the study suggests that, especially for women, being heavier, but still within a healthy BMI, deleteriously impacts on hireability ratings. The paper explores the gendered dimension of this prejudice by asking whether female employees at the upper end of a healthy BMI range are likely to be viewed more negatively than their overtly overweight male counterparts. The paper concludes by considering the implications of these findings. Here, iWilliams and Connell  note howusually slim, with ‘attractive’ faces” (p.178) (emphasis added). Similarly, Harris and Small’s . In. In18]. Scholars have also pointed to the gendered dimension of this “weight stigma”, such that women suffer significantly more prejudice than men when it comes to employment discrimination around their body shape and size  22] 9] [9]22] [ [9]22]  8]. Thi. Thi8]. Given the foregoing discussion, this research makes an original contribution to the extant literature on weight-based discrimination by examining not only the extent to which slightly heavy—but not overtly overweight or obese—female job applicants face discrimination in employee selection, but also whether any discrimination varies by proximity to the customer. It throws light on the question of whether female job applicants on the upper end of the medically healthy BMI range suffer more bias than male job applicants who are overtly and medically overweight. It is on these points that the research makes an original contribution to extant debates. Thus, two overarching hypotheses are presented:H1: A subtle simulated increase in adiposity for female applicants will have a significantly negative effect on hireability ratings.H2: The negative effect of a subtle simulated increase in adiposity for female applicants is stronger in customer-facing roles and weaker in non-customer facing roles.www.3d.sk). The images we selected for inclusion in the research were all taken under standardized lighting at the same distance. Faces were photographed from a 0˚ angle, with neutral expressions, hair pulled back and without any facial adornments. Inter-pupillary distance was also standardized. Both height and weight information for each subject was provided by the database. We settled on the faces of four men and four women  for experimental testing. The eight faces spanned a range BMIs . Note that both averages are within a normal, healthy BMI range.We initiated the experiment by drawing from a publicly available database of men’s and women’s faces (2 (SD = 0.79). The high-BMI female prototype had an average BMI of 24.06 kg/m2 (SD = 6.37). The low-BMI male prototype had an average BMI of 22.19 kg/m2 (SD = 2.52), and the high-BMI male prototype had an average BMI of 26.47 kg/m2 (SD = 3.27). The faces used in the low- and high-BMI prototypes of each sex were matched for age  ≤ 0.39, both p ≥ .70). Matching prototypes for age was important in this case to ensure that the transformation of the original faces reflected shape changes that were associated with BMI differences, and not age differences.In addition to the eight original faces, we created a set of low- and high-BMI female and male “prototypes”. These prototypes were used as “models” to transform the original faces so as to make them appear “heavier”. We created four prototypes: low- and high-BMI female prototypes and low- and high-BMI male prototypes. The prototypes were created by digitally averaging ten faces of individuals with the desired BMIs . The low2, resulting in a total of 16 stimulus faces . This method has been used in previous work to successfully alter perceptions of weight from faces  16] [16]9] [ [16]9] , we showThis research has clear implications for those responsible for hiring employees in the service sector, and also for job seekers who are seeking employment. For those responsible for hiring employees, it would seem important for them to recognize the potential for weight-based bias against applicants within the recruitment and selection process, even though such recognition may not necessarily change their views about such applicants. Thus, although Finkelstein et al  suggest A normal weight preference in hiring might not be viewed as discriminatory at all; an employer might very well feel that he could easily justify (even to himself) hiring a normal weight person in preference to an objectively overweight applicant … Hiring the overweight might be bad for business since the potential clients or customers either find the overweight person distasteful or associate the condition with deficient performance (p. 325).Consequently, whilst the ethical impulse in us might suggest that weight should not be an influential factor in recruiting employees, clearly our data, and other studies reported in the paper, demonstrate that, within the workplace, and particularly the service workplace, discrimination against women, even those on the upper end of the normal BMI range, remains real, current and widespread.In recognizing the extent of weight-based discrimination against applicants, Finkelstein et al  advocateFor jobseekers, the evidence might seem to suggest that they need a certain amount of pragmatism if they are seeking a customer-facing job in the interactive services sector, but from an ethical standpoint, we cannot advocate this conclusion. It would be fundamentally wrong to suggest that women (or indeed men) of any size—whether a medically healthy BMI, overweight or obese—should not seek a customer-facing job because of their appearance. Powerful societal standards of beauty should be actively worked against, rather than just accepted as a matter of fact. One of the best ways of countering these standards of beauty is to publicize studies such as this one that shine a light upon the unrealistic challenges that women face in a highly weight-conscious labour market and to draw much needed attention to the fact that the women respondents in our study were equally likely to rate “heavy but healthy” female job applicants just as negatively as the male respondents.This research is susceptible to the same set of critiques that are levelled against all studies in experimental psychology. The key challenge in laboratory-based studies is to approximate, as best as possible, “real life” conditions, but it is not possible to re-create them exactly. For example, in the real world, skills and qualifications are never held constant like they are in the present study. Furthermore, our respondents are analogues for hiring managers since they are role-playing. This is not, in itself, a devastating critique since analogue studies are widely accepted in experimental psychology. Furthermore, a now classic study by Bernstein et al.  verifiedDipboye  has suggAnother limitation that deserves mention relates to the non-random nature of the sample. The fact that it was composed in an online laboratory is not, in itself, a serious problem since it has already been proved that the results of web-based studies such as this one are generally comparable to those derived from the more traditional sampling methods . This liIt is also important to acknowledge that there may be concerns as to whether the study is capturing what it seeks to—the phenomenon of how slight increases in BMI affect hireability. For example, there may be an argument to suggest that this this study is, in fact, capturing differences in facial attractiveness rather than BMI. We would seek to allay these concerns to an extent by pointing out the work of Agerström and Rooth and Rooth  46] whi whi46] wFinally, it is worth noting that these results are limited to the experiences of white job applicants in the labour market. Holding the race constant in the test faces was important given that the use of multiple races in the experimental line-up could have resulted in biased ratings that were based on in-group favouritism and stereotypes . But in The results of this experiment are deeply unsettling from the viewpoint of gender equality in the workplace. It was found that, in service sector employment, women on the upper end of a normal and medically healthy BMI range face greater weight-based prejudice than men who are clearly and overtly overweight. In behind-the-scenes jobs in particular, the “heavy but healthy” women were rated lower on employability than the slimmer women, but there was no statistically significant difference between the overtly overweight men and their original faces. In short, a little weight gain for female job applicants is damaging to women’s job chances. These findings suggest quite clearly that women are at a distinct disadvantage compared to men in relation to their “gendered physical capital” .