The world’s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD’s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD’s diet is important.This study aims to design, develop, and test an artificial intelligence–powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge.The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations.A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness.The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers’ use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD’s diet. The progressive brain disorder of Alzheimer disease (AD) causes brain cells to degenerate and die slowly; degrades memory and thinking skills; and, eventually, destroys the ability to perform the simplest tasks of daily life . AD is tPeople with AD and related dementias (ADRD) are usually cared for by family members or friends, most commonly in their own homes. According to the Centers for Disease Control and Prevention, in 2019, more than 16 million Americans provided about 18.5 billion hours of unpaid care for family and friends with ADRD . Taking So far, there is no cure for ADRD. Limited medical treatments are available for ADRD’s symptoms, but good care from family caregivers can make the biggest difference in patients’ quality of life . Proper Voice-enabled technology, which is becoming popular worldwide, offers a potential solution. In the United States, the number of people who use voice assistants such as Amazon’s Alexa or Apple’s Siri is rapidly increasing; in 2019, more than 111 million people in the United States used a voice assistant at least monthly, equivalent to 39% of internet users and 34% of the total US population . Amazon’Alexa’s capabilities can be extended with “voice skills,” apps developed by third-party vendors. As of March 23, 2020, there were more than 70,000 skills available. These “skills” enable users to interact with their Alexa-enabled devices using natural language in a variety of ways including gaming; shopping; and gathering information about news, weather, travel, health, and fitness.As voice-activated technology has become increasingly popular, researchers and engineers have begun to develop it for use in health care domains, ranging from health news briefs, nutritional guides, and fitness trackers to programs for meditation and yoga. The Mayo Clinic, for example, has launched a “First Aid” Alexa voice skill that answers questions about how to treat common mishaps such as cuts and burns . It offeIn this paper, we present the design, implementation, and preliminary testing of an artificial intelligence–powered voice assistant to provide personalized education and guidance for caregivers on food, nutrition, and cooking for a loved one with ADRD. This voice assistant gives the ADRD caregiver continuous access to useful tips about food, nutrition, and eating behaviors. It also recommends food and meals. The tips and recommendations are personalized, specific to the patients with ADRD’s condition including the patient’s ADRD stage, preferences, and medical conditions. Moreover, the assistant’s recommendations are specific to the caregiver, based on the caregiver’s time limits, financial ability, and education level. Because voice is our most common mode of communication, a voice assistant provides a natural way to engage with technology that requires minimal training. This is especially valuable for caregivers who are older adults, who account for more than one-third of caregivers, and who may find it difficult to use other forms of technology that require fine motor skills, hand-eye coordination, or good vision.The goal of our project is to design a voice-enabled intelligent assistant to support effective diet management for ADRD caregivers. The voice assistant is built on top of a comprehensive personalized ADRD diet care knowledge base, a semantics-based knowledge graph reasoning engine, and Amazon’s cloud-based voice service Alexa.As illustrated in IS-A) relationship and the type relationship. This allows the DM module to better identify ADRD food management–relevant entities.This component allows the user to interact with the voice assistant. It receives the user’s voice input from Amazon Alexa. Alexa converts the user’s speech into text using automatic speech recognition and natural language understanding. The UID module matches the user’s voice, converted to text, with a predefined intent. The intent corresponds to a system action that fulfills the user’s spoken request. To effectively identify the user’s intent, we extend users’ utterances with synonyms and ontology concepts  from our knowledge base. Because the system focuses on diet management, we have chosen top-level food-, nutrition-, and diet-related classes, and classes or instances that are connected to the chosen top-level classes by two major relationships of an ontology: the hierarchical specialization/generalization  from the user. It then tries to find the response that both addresses the user’s request and meets the constraints of the conversation topic and context. The response requires knowledge support from the back-end AD diet management component. This component bridges the gap between high-level application logic and low-level dialogue specifications. It allows the dialogue to be dynamically adapted according to the current high-level context.The voice assistant’s “brain” is a comprehensive knowledge base, which stores the knowledge that informs the answers, recommendations, and tips that the voice assistant provides. To construct this knowledge base, we conducted an extensive review of the scientific literature on ADRD home care and on food and nutrition tips for ADRD care -12,20-23An ontology represents a domain of discourse explicitly. It defines the concepts and relationships used to describe and represent the domain . It defiBesides concepts (classes) and relationships (properties) defined in the ontology, the knowledge base includes rules based on professional medical guidelines related to AD diet. We have collected AD general caring guidelines and food and nutrition guidelines such as from the National Institute on Aging , AlzheimThe back-end ADRD diet management component receives the user’s request as input and calls the corresponding back-end service to generate a response that is sent back to the RG module in the conversation management component. The back-end services are built to enable context-aware personalized recommendations, education, and question answering. The foundation of this component is a description logic (DL)  query-anA backward-chaining algorithm is used for automatic reasoning. Backward chaining  is a goaSupposing a being of class D and A, the following backward chaining application in equation 3 illustrates how to prove a being of class B.The reasoning engine takes the AD diet management rules in SWRL format , facts fWe have implemented a prototype of the voice assistant as an Amazon skill “ADRD-DietAssistant.” We have conducted a set of preliminary evaluation tests to assess the voice assistant’s technical viability and effectiveness.Front-end implementation:Alexa Skills Kit (ASK):Dialogue design, intent definition, utterances to activate intents, slot customizationThe Amazon Lambda service:Runtime: Node.js 12.14.1Software development kit (SDK) to communicate with Alexa: ASK-SDKLogging service: Amazon CloudWatch LogsThe back-end diet management server is configured as follows:Processor: Intel Core i-7-4770 CPU @ 3.40 GHz (8 CPUs), ~ 3.4 GHzRAM: 32 GBOperating system: Windows 10 Home 64-bit Programming Language: Java 8, Java Development Kit 1.8Semantic service: OWL Application Programming Interface and SparkJavaDatabase: MySQL: The back-end AD Diet ManagementThe voice assistant can support a variety of food and nutrition recommendations, education, and planning services including tips on proper diet, handling challenges in eating, food item and nutrition explanations, meal suggestions, and recall of daily and weekly diet histories. In a conversation, different service categories can be integrated to serve the user’s requests.Utterance 1 is an invocation that activates the diet assistant voice skill. Once the skill is activated, the voice assistant greets the user (Utterance 2). A conversation then follows based on the user’s requests. The voice assistant triggers a specific intent designed to handle the user’s specific request. In Utterance 3, the user asks the voice assistant whether a certain food is good for the patient. This triggers the back-end service to check whether the food item—cheese in this case—is good as a snack or not. The variables collected from this utterance are “cheese” and “snack,” which are passed on to the system’s back end. The system’s back end has an intent handler that then checks to determine whether “cheese” is good as a “snack” for people with Alzheimer and diabetes, using facts and rules stored in the ontological knowledge base. It then prepares a speech to be sent back to the user as a response, as shown in Utterance 4. In Utterance 5, the user asks for a good snack suggestion. The back end then uses intent chaining to find a healthy “snack.” The response is shown in Utterance 6. Another sample conversation is shown in To provide a satisfying response to a user’s requests, the back-end diet management server will consider multiple factors that affect the patient’s diet decisions, such as ADRD nutrition requirements, food availability, cooking time, servings, preferences, medical restrictions, and cultural and religious constraints. The system extracts all these constraints from the user’s profile, context, and health guidelines.For the recipe recommended in The Mediterranean–Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (MIND) diet  encouragThe MIND diet also encourages limiting one’s consumption of butter and margarine, cheese, red meat, fried food, pastries, and sweets.The US Department of Health and Human Services Dietary Guidelines  state thThe American Diabetes Association Diabetes Guidelines  state thThe voice assistant also checks for other factors affecting the user’s diet decisions, such as the user’s (patient’s or caregiver’s) preferences, allergies, religious constraints, and so forth.We have performed a set of qualitative evaluations to test the system’s performance. The testers, who included one faculty member and four graduate students on our research team, performed 180 conversations with the voice assistant in our research lab. These conversations were invoked by the testers’ requests. Each of the requests belonged to one of the following service categories: food item and nutrition explanations, meal and recipe suggestions, and tips for proper diet. Otherwise, testers had maximum freedom and flexibility in talking to the voice assistant.In our testing, we defined a threshold, the maximum number of turns in the dialogue between the user and the voice assistant, to limit the dialogue’s length. If a conversation exceeded this limit and the user did not receive a satisfying response, the conversation was noted as failed. In our evaluation, we set the threshold of maximal dialogue turns at 20. Given this threshold, the dialogue success rate is defined by equation 4:The correctness of information provided by the voice assistant is defined in equation 5. Correctness is determined by manually checking whether the recommended content is correct on the basis of the medical guidelines and consistent with a user profile artificially generated for the test, that is, whether the recommendation satisfies the user’s request and is consistent with the patient’s and caregiver’s profile, following the constraints and guidelines in the knowledge base:Healthy eating may help slow and even prevent the progression of ADRD and subsequently improve the health status and quality of life in older age . To assiThis study has limitations. The qualitative evaluation was performed within a laboratory setting. Whether or not actual caregivers in a natural setting will use the assistant in ways similar to those of researchers in the laboratory remains for further research. We plan to deploy the voice assistant in a natural setting with ADRD caregivers and patients with early stage ADRD as users. More comprehensive user studies will allow us to evaluate the voice assistant’s usability, users’ satisfaction, and outcomes for improved health and quality of life. We will also conduct focus groups to collect users’ feedback about their knowledge needs to improve our knowledge base.To increase the dialogue success rate, we propose several approaches to improve the flexibility and accuracy of matching:The voice assistant can provide more oral guidance during conversations so that users have a better understanding of the voice assistant’s expectations.Developers should define more combinations of utterances and synonyms for each intent and its slots to improve the matching rate.Although Amazon uses machine learning to match a user’s intention with defined intent, this learning is limited, and matching performance largely depends on keywords and sentences provided at the design stage. We will design our machine learning algorithms based on domain knowledge and user feedback to improve accuracy of understanding.We will extend our knowledge base with more relevant knowledge.In addition, we plan to capture conversations and use them for machine learning to improve conversation accuracy. If we can solve potential issues of privacy and security with users’ approval, in the future, we will collect and use large amounts of conversation information for increased accuracy.Voice is the most natural and powerful mode of human communication. Our voice assistant gives users a natural, interactive interface, and it does not require the technical background that might be a barrier to senior caregivers’ use of new technologies for daily care tasks. This may enhance caregivers’ engagement in ADRD diet care and reduce caregiver burden, improving the care of patients as well as caregivers’ own health and well-being.


Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our sample, we identify topics that appear more often within users’ social feeds than they do globally among all posts. We also present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic’s global prevalence from biased individual perceptions. We characterize the polling estimate and validate it through synthetic polling experiments on Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort perceptions and presents approaches to mitigate this bias. Individuals within social networks rarely observe the network as a whole; rather, their observations are limited to their social circles. Here we show that network structure can distort observations, making a trait appear far more common within many social circles than it is in the network as a whole. One of the better known sources of bias is the friendship paradox in social networks8, which states that people are less popular than their friends are, on average. Consequences of friendship paradox can skew how we compare ourselves to friends: people tend to be less happy than their friends are9, and researchers tend to have less impact than their co-authors do10, on average. In fact, any trait correlated with popularity is likely to be misperceived12. This may explain why adolescents systematically overestimate how much their peers drink or engage in risky behaviors5 and why social media use is often associated with negative social comparisons13.We observe our peers to learn social norms, assess risk, or copy behaviors. However, these observations can be systematically biased14: your friends (or followers) have more friends (or followers) than you do, on average. Empirically, this effect can be quite large, with upwards of 90% of social media users observing that they have a lower in-degree and out-degree than both their friends and followers15. However, the conditions under which these four variants of the paradox exist have not been comprehensively analyzed. We carry out the analysis to show that while two variants of the friendship paradox occur in any directed network16, the remaining two exist only if an individual’s in-degree and out-degree are correlated.In contrast to friendships, many online social networks are directed. On Twitter, for example, we subscribe to, or follow, others to see their posts, but the information does not flow in the opposite direction, unless those people also follow us back. For convenience, we refer to people whose posts we see in our social feeds as our friends, and those who see our posts as followers. Clearly, this nomenclature does not imply a bidirectional friendship relationship. An individual’s in-degree is the number of his or her friends, and the out-degree is the number of followers. The asymmetric nature of links in directed networks leads to four variants of the friendship paradox17, correlation between nodes’ trait and their out-degree amplifies this perception bias.Friendship paradox can systematically skew individual’s observations of the network’s state. We consider directed networks where nodes have a trait, such as gender, political affiliation, or whether they used a certain hashtag in their posts. The trait’s global prevalence is simply the fraction of all nodes with that trait. On the other hand, its observed prevalence is the fraction of friends that have the trait. In networks where the more influential (higher out-degree) nodes are likely to have the trait, its observed prevalence will be substantially higher than its actual prevalence. Our analysis shows that, similar to the generalized friendship paradox in undirected networksIn reality, an individual’s perception of a trait is shaped by its local prevalence among his or her friends. In this paper, we identify a new paradox in directed networks, as a result of which a trait will appear to be significantly more popular locally among an individual’s friends, than it is globally among all people. We show that this effect is stronger in networks where higher out-degree nodes with the trait are connected to nodes with a lower in-degree.Surprisingly, although individual observations are biased, we can still robustly estimate the global prevalence of the trait. We present a polling algorithm that obtains a statistically efficient estimate of a trait’s global prevalence, with a smaller error than alternative polling methods. Proposed method leverages friendship paradox to reduce the error of the polling estimate by trading off the bias of the estimate and its variance. We analytically characterize this trade-off and provide an upper bound for the variance.We also show that perception bias can be large in a real-world network. To this end, we extracted a subgraph of the directed Twitter social network and collected messages posted by users within this subgraph. Treating the occurrence of particular hashtags within messages as traits or topics enables us to measure the perception bias. We identify hashtags that appear much more frequently within users’ social feeds than they do among all messages posted by everyone, leading users to overestimate their prevalence. We also validate the performance of the proposed polling algorithm through synthetic polling experiments on the Twitter subgraph.This paper elucidates some of the non-intuitive ways that directed social networks can bias individual perceptions. Since collective phenomena in networks, such as social contagion and adoption of social norms, are driven by individual perceptions, the structure of networks and the paradoxes endemic in them can impact social dynamics in unexpected ways. Our work shows how we can begin to quantify and mitigate these biases.G = , with {V} nodes and {E} links. A link  pointing from i to j indicates that i is a friend of j or equivalently, j follows i. Here, the direction of the link indicates the flow of information. The out-degree of a node v, do(v), measures the number of followers it has, and its in-degree, di(v), the number of friends.Consider a directed network X, Y, and Z that correspond to different node sampling methods. A node v with out-degree do(v) has that many followers, or equivalently, v is a friend to do(v) number of nodes. Therefore, a node Y that is obtained from V by sampling proportional to out-degree of nodes is called a random friend. Similarly, a node v that has di(v) links pointing to it is a follower of di(v) other nodes. Therefore, a node Z that is obtained from V by sampling proportional to in-degree of nodes is called a random follower. Below, we formalize these terms.We define three random variables, X is a uniformly sampled node from V:Random node Y is a node sampled from V proportional to its out-degree:Random friend Z is a node sampled from V proportional to its in-degree:Random follower X: For any directed network, the average in-degree 14. The first two, state that (1) random friends have more followers than random nodes do, and (2) random followers have more friends than random nodes do (on average). The magnitudes of these are set by the variance of the in-degree and out-degree distributions of the underlying network. Mathematically, these two friendship paradoxes can be stated asY has more followers than a random node X, on average:Random friend Z has more friends than a random node X, on average:Random follower For the derivation, please see Supplementary Note Four different variants of the friendship paradox exist in directed networksY has more friends than a random node X, on average:Random friend Z has more followers than a random node X, on average:Random follower For the derivation, please see Supplementary Note The remaining two variants of the friendship paradox state that (3) random friends have more friends than random nodes do, and (4) random followers have more followers than random nodes do (on average). In contrast to the first two variants of the paradox stated above, the remaining two variants require positive correlation between the in-degree and the out-degree of nodes in the network:mentclass2pt{minim16 utilizing vector norms.Equations To give additional intuition, Fig. f: V → {0, 1}). Such binary functions are useful for representing, among others, voting preferences (Democratic or Republican), demographic characteristics , contagions (infected vs. susceptible), or the spread of information in networks (using a particular hashtag or not).When nodes have distinguishing traits or attributes, the friendship paradox can bias perceptions of those attributes. For simplicity, we assume that each node has a binary-valued attribute  = 1, its expected value is The global prevalence of the attribute in a directed network is given by Y. On Twitter, this translates into how many people see the topic in their social feed, since the feed aggregates posts made by friends. Under some conditions, the perceived prevalence of the attribute σf is the standard deviation of the binary attributes  are more likely to possess some trait  of an individual v ∈ V about the prevalence of an attribute f among his or her friends:v. We define local perception bias as the deviation of the expected perception of a trait of a random individual from its global prevalence:Blocal, we define the attention that a node v ∈ V allocates to each of her friends:18, making them less likely to see any specific friend’s post19. This allows us to succinctly express the expected perception of a random node X as  of the friend U times the attention that the follower V pays to that friend. Note that for simplicity we assumed that nodes divide their attention uniformly over all friends, though the analysis can be extended to weighted networks, where weights model non-uniform attention, with individuals paying more attention to their more important or influential friends.One problem with using obal Eq.  to measumentclass2pt{minimBlocal is a refinement of global perception bias Bglobal, which accounts for how individuals divide their attention in the network. Indeed, if the attention of followers is independent of the attribute of their friends, both measures are the same. Formally, Bglobal and Blocal are equal if and only if the attribute f(U) of U and attention U, V) are uncorrelated, i.e.,Local perception bias mentclass2pt{minimBlocal ≥ 0, when the folowing conditions are met (see SI):Bglobal ≥ 0 . The seBglobal and Blocal can differ significantly and even disagree, with one measure indicating that individuals are underestimating and the other indicating that they are overestimating the prevalence of an attribute. We analytically characterize the two cases where this occurs . Similarly, when Bglobal is positive, Blocal can be negative when few of the nodes with an attribute have high attention followers, leading to a negative correlation between the attribute of a friend and the attention of his or her follower. The discrepancy exists because Bglobal makes a mean-field approximation by assuming that the expected attribute value among friends of a random node X is equal to the expected attribute value of a random friend Y sampled from the entire network. In contrast, Blocal is a higher resolution measure that takes the underlying network structure into account via the correlation between the attribute of a friend and the attention of a follower.Under other conditions Relation to inversity in undirected networks: Local perception bias is related to the concept of inversity20, which is defined as the correlation coefficient of the two random variables d(U) and 1∕d(V) where, d denotes the degree, and  is a uniformly sampled link in an undirected network. Although the mathematical form of inversity is reminiscent of degree assortativity21, it does not convey the same information. Kumar et al.20 shows that the relation between global and local versions of the friendship paradox in undirected networks is characterized by inversity and not assortativity. Specifically, when inversity is positive, then the local version of the friendship paradox is larger in magnitude than the global version of the friendship paradox in undirected networks. This result can be obtained by extending our analysis to undirected networks and setting f = d in the expressions for Bglobal and Blocal. In fact, Eq.  in our paper generalizes their findings to directed networks and arbitrary exogenous attributes f.act, Eq.   = 1 if a user v used the hashtag h in his or her posts.We used data from Twitter (see the “Methods” section) to compare the actual and perceived popularity of hashtags  mentioned in text posts. We treat each hashtag Blocal for all hashtags. Although its peak is at zero, the distribution is skewed, with 865 hashtags having a positive bias, meaning that they appear more popular than they really are. Measurements of individual’s perception shows that most users in our sample overestimate how popular hashtags are , do(X)} < 0), or the attention of their followers is diluted because they follow many others  or more retweets . Many of these hashtags are actually among the top-20 most popular Twitter hashtags , but due to the structure of the network, they appear less popular to users. This occurs either because people who use these hashtags do not have many followers  variance of the estimate The key idea behind our follower perception polling (FPP) algorithm is to sample individuals who have more friends, as this allows them to aggregate more information. According to the friendship paradox (Eq. ), randomIntent polling (IP): asks random users whether they used a hashtag : asks random users what fraction of their friends used the hashtag , FPP produces biased estimates for each hashtag , FPP outperforms IP in more than 80% of the cases, and it outperforms NPP in more than 55% of the cases.Figure tag Fig. , given btes Fig. . Hence, ags Fig. . Increasags Fig. . Howeverλ2, the second largest eigenvalue of the degree-discounted bibliographic coupling matrix. For the Twitter data λ2 = 0.5984. Equation (The variance of the polling estimate (Eq. ) is bounEquation  with thiFollower sampling heuristic: The FPP algorithm assumes that followers are obtained by sampling nodes with probabilities proportional to their in-degree, or equivalently, sampling links at random from the network and then selecting the endpoint of the link. This is feasible when the entire network is known, or when the links have integer IDs, which can be uniformly sampled from a range of IDs. In many cases, neither strategy is feasible, either because the network is too large, or it does not allow access to individually indexed links. In that case, we can use the following heuristic to sample followers: select a node at random and ask her to nominate a random follower. Supplementary Note 22.11, and to dramatically underestimate the size of the minority group6. These effects arise due to the friendship paradox, which can also bias the observations individuals make in directed networks. Our analysis identifies the conditions under which friendship paradox can distort how popular some attribute or behavior  is perceived to be, making it appear several times more prevalent than it actually is. The following two conditions amplify perception bias: (1) positive correlation between the attributes of individuals and their popularity (number of followers in a directed network) and (2) positive correlation between the attributes of individuals and the attention of their followers. The first condition suggests that bias exists when popular people have the attribute, for example, engage in risky behavior, have a specific political affiliation, or simply use a particular hashtag. Their influence is amplified when they are followed by good listeners, i.e., people who follow fewer others and thus are able to pay more attention to the influentials. These conditions can be generated by biases in preferences during network formation, driven for example, by homophily23.Social networks can exhibit surprising, even counter-intuitive behaviors. For example, previous work has shown that the “majority illusion” may lead people to observe that the majority of their friends has some attribute, even when it is globally rare24. For example, the #MeToo movement has grown into an international campaign to end sexual harassment and assault in the workplace by highlighting just how endemic the problem is. It spread through online social networks as women posted their own stories of harassment using the hashtag #metoo. Perception bias may have amplified the spread of such hashtags by making them appear more common and thus easier to use.We validated these findings empirically using data from the Twitter social network. We measured perceptions of the popularity of hashtags, i.e., words or phrases preceded by a ‘#’ sign that are frequently used to identify topics on Twitter. Such hashtags serve many important functions, from organizing content, to expressing opinions, to linking topics and people. We measured a hashtag’s global prevalence as the fraction of all people using it, and its perceived popularity as the fraction of friends using it. Our analysis identified hashtags that appeared several times more popular than they actually were, due to local perception bias. Such hashtags were associated with social movements, memes, and current events. Interestingly, as our data was collected in 2014, some of the most biased hashtags were #icebucketchallenge and #alsicebucketchallenge, the explosively popular Ice Bucket Challenge. Perception bias could have potentially amplified their spread, as well as the spread of other costly behaviors that require social proofWe also presented an algorithm that leverages friendship paradox in directed networks to estimate the true prevalence of an attribute with smaller mean-squared error than other methods. In essence, the idea behind the algorithm is that perceptions of random followers should have a smaller variance compared to the perceptions of random individuals, because random followers are more informed than random people are, since according to friendship paradox they tend to have more friends. Empirical results confirm that the proposed algorithm outperforms other widely used polling algorithms.Our work suggests that one way to mitigate perception bias is to alter the local network topology to allow more information to reach the low-attention users. This opens up new research avenues on how link recommendation can alleviate perception bias. However, our empirical study has limitations, namely, the nature of the subsample of the network we considered. Social networks are huge, necessitating analysis of subgraphs sampled from the entire network. However, by leaving out some nodes, data collection process itself may distort the properties of the sample. Specifically, since we observed only the outgoing links from the seed nodes, we do not have information about the followers of these nodes. Addressing the limitations of analysis imposed by sampling is an important research direction. Despite this limitation, our work shows that friendship paradox can lead to surprising biases, especially in directed networks, and suggests potential strategies for mitigating them.The dataset used in this study was collected from Twitter in 2014. We started with a set of 100 users who were active discussing ballot initiatives during the 2012 California election and expanded this set by retrieving the accounts of the individuals they followed and reached a total of 5599 users. We refer to these individuals as seed users. Next, we identified all friends of the seed users, collecting all directed links that start with one of the seed users. We then collected all posts made by the seed users and their friends—over 600K users in total—over the period June–November 2014. The posts include their activity, i.e. tweets and retweets. These tweets mention more than 18M hashtags. With this data-collection approach, seed users are fully observed , and their friends are only partially observed (only their activity).Table Due to the relatively large variance of the in- and out-degree distributions, the expected out-degree of a random friend ((see Eq. ). Note a25. This is because in expectation polling, an individual names the candidate more popular among her friends, thus summarizing a number of individuals in the social network, rather than provide her own voting intention. Our FPP algorithm is motivated by27, which shows that polling methods asking individuals to summarize information in their neighborhood outperform polling methods that ask only about the attribute of each individual. Dasgupta et al.26 studied the polling problem analytically in the context of an undirected network and, proposed a method to obtain an unbiased estimate of the global prevalence with bounds on its variance. The analysis of the FPP algorithm for directed graphs is motivated by these results in ref. 26 for undirected social networks. Nettasinghe and Krishnamurthy27 proposed to ask the simple question “What fraction of your neighbors have the attribute 1?” (neighborhood expectation polling) from randomly sampled neighbors (instead of random nodes) on undirected social networks. In this case, sampled individuals will provide the average opinion among their neighbors. Further, since random friends have more friends than random individuals, this approach would yield an estimate with a smaller variance than asking it from random nodes. Motivated by these works, the FPP algorithm exploits the friendship paradox on directed networks to obtain a statistically efficient estimate of the global prevalence of an attribute using biased perceptions of random followers.The accuracy of a poll depends on the method of sampling respondents and the question asked of them. For example, in the case of estimating an election outcome, asking people “Who do you think will win?” (expectation polling) is better than “Who will you vote for?” (IP)qf(Z) of random followers Z instead of attributes f(X) of random individuals X. However, it is not guaranteed that the estimate Bglobal.Recall that in order to reduce the variance, the FPP algorithm polls perceptions The bias of the estimate Bglobal of the algorithm with a reduced variance and thereby achieve a smaller mean squared error. Also, we highlight that FPP algorithm can be modified to generate an unbiased estimate by replacing Eq. 26 for undirected social networks where, queried individuals provide a weighted value of their friends’ attributes in a manner that results in an unbiased estimate. This estimate is useful in contexts where unbiasedness is preferred over mean-squared error to assess the performance of the estimate. However, this does not result in an intuitive and easily implementable algorithm similar to the FPP algorithm, since the modified estimate Hence, the same factors  = ∑v∈VAA which corresponds to the number of mutual followers of i and j. Hence, the weight of the link between two nodes i and j in B is the number of individuals who follow both of these nodes. This conveys the similarity of i, j in terms of the number of mutual followers. However, when determining the similarity of two nodes i, j using B, a mutual follower with a large number of friends (a likely scenario), is weighted the same as a mutual follower with a small number of friends (a rarer scenario). Hence, the latter type of mutual follower should be given more weight compared to the former type when evaluating the similarity of two nodes. Similarly, the number of followers of i and j should also be taken into consideration when assessing their similarity. Based on these observations, Satuluri and Parthasarathy29 proposed the degree-discounted bibliographic coupling matrixDo and Di are the N × N dimensional diagonal matrices with Do = do(i) and Di = di(i), respectively. The  element of Bd isi, j by their out-degrees and each mutual follower k by her in-degree. Please see refs. 30 for more details on the degree-discounted bibliographic coupling.Before analyzing the variance of the polling estimate Bd is connected, non-bipartite, thenM = ∑v∈Vdi(v), λ2 is the second largest eigenvalue of Bd, f is the N × 1 dimensional vector of binary attributes 31. Hence, if the nodes in the network G =  cannot be clustered into distinct groups based on their mutual followers (i.e. bibliographic similarity) then, the variance of the algorithm will be smaller .This result shows that the variance of the FPP algorithm depends on the correlation between the out-degrees and attributes Further information on research design is available in the Supplementary InformationPeer Review FileReporting summary


The interrelationships between nature, health, and wellbeing are increasingly recognized and incorporated into therapeutic interventions. Care farming, the concept of utilizing agricultural places and practices for providing care, therapy, and rehabilitation, is a paradigmatic example of this shift. This mixed method study empirically evaluates the efficacy of care farming as an intervention for individuals affected by traumatic grief, a complex experiential condition. Both quantitative and qualitative results suggest this care farm intervention was beneficial, yielding significant reductions in subjective distress to grief intensity. The study's findings add to the growing body of evidence on care farming and support green care as a therapeutic potential for individuals affected by traumatic grief. •Few interventions have been successfully tested with those suffering traumatic grief. Care farming is a potential alternative means toward provision of care for the traumatically grieving population.•After a two-day, 10 h intervention, participants experienced significantly less subjective trauma and distress in pre/post tests using the TGI psychometric measures.•Rich qualitative data reveal that participants felt supported, connected, and yielded therapeutic benefit.•Given the lack of consensus on the use psychotropic medications for grief, green models, such as care farming, provide to care that is not limited to a medical model. However, this diversity can be conceptualised as having “as their central dimension the engagement of the individual with nature (in a structured and facilitated way) to provide a benefit to health” , p. 223.Recent scholarship has demonstrated an interest in the interfaces between grief and green-care type interventions . IndividThe purpose of this mixed methods study is to empirically investigate the efficacy of a particular form of green care, care farming, as an intervention for individuals affected by traumatic grief, drawing on both quantitative and qualitative approaches. The conceptual contexts which framed this study are introduced, drawing on the interdisciplinary literature that discusses ideas of “therapeutic landscapes” and “therapeutic communities,” before contextualising “care farming” and how this intervention might be applied to the interface of trauma and grief.2Care farms have been described as both “therapeutic landscapes”  and “the2.1The conceptual framework of “therapeutic landscapes”  has beenThe framework draws attention to the very geography of therapeutic interventions and communities, focusing on place as active and constitutive of health, while recognizing that it is not possible to separate experiences of health and wellbeing from the places in which they are experienced . InterveMany of the ideas associated with the therapeutic landscape concept are also reflected in the therapeutic community concept . TherapeThough having a long history see , present3Care farming, the concept of combining agricultural places and practices with the provision of health, social, and educational services, has arisen as an innovative modality for providing forms of care, therapy, and rehabilitation . As mentCare farming has been defined as “the use of commercial farms and agricultural landscapes as a base for promoting mental and physical health through normal farming activity” , p. 247.Participation in care farming has been shown to have a wide variety of benefits, including decreasing anxiety, stress , and depHuman-animal relationships play a significant role in the care farm experience, with Care farming has been widely used for a variety of populations and conditions. A novel community-based care farm has been established that aims to specifically help those experiencing traumatic grief. This study explores the effects of this care farm on study participants.4Grief that results from the death of a loved one under traumatic circumstances, particularly when the relationship is highly dependent, may be experienced differently from grief related to more natural and expected deaths . HomicidTraumatic grief is a complex experiential condition, having biological, psychological, social, and cultural facets. It can incite a long-term and intense form of distress , puttingAttempts to grapple with the complex intersection of trauma and grief have seen an increasing move to “medicalize” those affected with the creation of Persistent Complex Bereavement Disorder in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. This move has been celebrated for capturing grief-related problems unrelated to depressive or posttraumatic stress symptoms , but cri5Our methodology follows a mixed methods approach in order to ‘gain a better to gain a better understanding of the connections or contradictions between qualitative and quantitative data’ : 75. Mix5.1Following Institutional Review Board approval via Arizona State University, recruitment materials were published for two weeks on the social media sites of several non-governmental agencies that help families facing traumatic grief. Prospective adult participants were invited to take part in a study whose stated intention was to understand the effects of a care farm intervention on their subjective experience of trauma and grief. They were offered a web link for registration and initial intake, this included a section obtaining participant's informed consent to take part in the research. Eligible participants were then contacted to schedule the in-person intervention. Given the potential for sensitive topics to be discussed during the course of participants' involvement in research, a list of nonprofits specifically aimed at supporting grieving individuals and families was provided to participants as well as a resource brochure on grief.One to two weeks prior to arrival at the care farm, respondents provided demographic and loss-related information and completed a standardized traumatic grief measure, chosen to quantify the subjective intensity of their psychological state both pre and post intervention. Two to five weeks after the intervention, they completed the measure again, and two to five weeks after that, they were invited to participate in a qualitative, semi-structured interview with a research team member who was not involved with the delivery of the intervention. At each of these stages, informed consent was obtained from participants, that they were comfortable taking part in the research.5.2The carefarmThere are several formally designated “restorative spaces” that together constitute the therapeutic landscape of the carefarm including “The Quiet Place,” a small grotto under a large Ash tree by a waterway where families paint stones and leave them in the rock wall; a heavily treed river area with swings, a firepit, and kayaks; a gazebo set in the middle of a small pasture; and various spaces for equines who, unlike the other animals who roam the entire carefarm freely, graze in enclosed pastures for safety. Though the carefarm is secularized, the guiding principle is unitive “ahimsa,” or dynamic compassion, the foundation upon which many spiritual traditions are built . The con5.3The intervention lasted for 10 h over the course of two consecutive days utilizing a model of green care  endorsed5.4Traumatic Grief Inventory Self-Report. The Traumatic Grief Inventory Self-Report (TGI-SR) is commonly used in research with this population to assess a person's experiences of traumatic grief. The TGI-SR is an 18-item measure reflecting the criteria for both Persistent Complex Bereavement Disorder (PCBD) and Prolonged Grief Disorder (PGD), a related proposed disorder (disorder . Items aSeven related items were added asking about coping with difficult grief-related experiences rather than the severity of the experiences themselves. These items were matched to TGI-SR questions (e.g. “I feel bitterness or anger related to his/her death” was followed by “I can cope with my bitterness or anger related to his/her death”) and then summed separately to create an overall coping score, with higher scores indicating better coping.Qualitative interviews. Semi-structured interviews were used to develop a fuller understanding of the opportunities and challenges associated with utilizing care farming as a therapeutic intervention for people experiencing grief and bereavement. Interviews were conducted by a member of the research team who was not involved in the delivery of the intervention. Interviews lasted for an hour, on average, and were conducted via phone or online video-call. Evidence suggests that qualitative telephone interviews can limit emotional distress because of the comfort experienced through virtual communication (nication . Open-ennication : 79. All66.1SD = 11.37) years. Religiously, most identified as Christian (36.4%) or agnostic or atheist (27.2%). Most participants were married or partnered (63.6%) and employed (81.8%). The most frequently selected categories for annual family income were $75,000 to $100,000 (31.8%) and $50,000 to $75,000 (36.4%).The quantitative sample consisted of 22 bereaved individuals, three couples and 16 who came as individuals. The sample was 68.2% female, predominantly of White European descent (77.3%), and with a mean age of 42.41 (SD = 4.36) years. The age range of the loved one at the time of death was most frequently before/during birth to three years (36.4%) and 21 years and older (36.4%). The majority (72.7%) reported having spent time previously on a farm and around farm animals; 40.9% reported they were taking psychotropic medications at pretest. When asked to rate how valuable the carefarm experience was at posttest, 95.5% chose “extremely valuable” and 4.5% chose “valuable.” Demographic and loss-related data are provided in Most participants had experienced the death of a child (77.3%) and the remainder had experienced the death of a sibling (22.7%). Most reported that the death had been sudden and unexpected (86.4%), while others reported that it was the result of a long-term illness (4.5%) or marked “other” and indicated that the death was from a terminal illness within a year of diagnosis (9.1%). The mean time since the loss was 3.39 (6.2SD = 11.08) at pretest and 50.09 (SD = 8.89) at posttest. The scores at both times were normally distributed, with skewness and kurtosis values under 1, and had no outliers. Paired-samples t-tests showed a significant reduction in scores from pretest to posttest, t(21) = 5.57, p < .0001, 95% CI . Initially, 50% of scores were above the clinical cutoff of 61. At posttest, this figure had fallen to 18.2%.Cronbach's alpha, which is a measure of a scale's internal consistency, or the degree to which scale items measure the same construct , was gooSD = 4.35) and the mean posttest score was 25.91 (SD = 3.90). Paired-samples t-tests showed a statistically significant increase in coping from pretest to posttest, t(21) = −5.86, p < .0001, 95% CI .The internal consistency for the coping scales created to complement the TGI-SR was low as measured with Cronbach's alpha . These distributions approached normal but had skewness values of −1.15 at both times and kurtosis values of 1.26 at pretest and 1.74 at posttest. There was one outlier in the posttest coping distribution, on the low end. The mean pretest coping score was 22.05  = .05, p = .82, or at posttest, F = .35, p = .56.Because time since the loss is often thought to impact the intensity of grieving, a univariate regression analysis was run to determine whether this was true in the current sample. Time since the loss was not a significant predictor of TGI-SR score at pretest, The mean TGI-SR and coping scores were broken down according to whether participants had experienced the death of a child or sibling. Within the child loss group, mean scores were further broken down according to the age of the child at death. Bereaved siblings (n = 5) experienced a higher mean reduction in TGI-SR scores from pretest to posttest (22.6) than did bereaved parents (11.34).6.3Of the 22 participants who completed the TGI-SR, 21 were interviewed . Across the corpus of interview data, three themes stood out as particularly important when participants reflected upon their experiences at the carefarm and how it affected their grief. Firstly, the “restorative spaces” of the carefarm: the landscape, atmospheres, and natural milieu of the farm, and how people interpreted and interacted with these elements, in metaphorical, embodied, and sensuous ways. Secondly, the “community” of the carefarm: being in a place where grief is accepted and depathologized, with others who could empathise and relate, and where it was acceptable to talk about grief and trauma. Thirdly, the connection to animals: both the animals as a metaphor and signifier that instilled hope, but also the opportunity to develop empathy and compassion through encounters and relationships with the animals. Particularly, it was the ability to do all of this in parallel with the focused counseling, the animals, nature, and a sense of community creating beneficial opportunities for contemplation, reflection, and depressurisation that enhanced therapeutic processes.6.3.1“I've been through regular counseling before where you sit in a room. It's just so different when you're outside. I guess it's just enough of a distraction to let you relax a little bit. So I'd be able to open up a little bit more, it just helps you, it helps me relax and feel more comfortable and open.” – Anna“Thing I liked is that it was not clinical. It didn't feel like you were in any kind of facility.” - JoeWhen reflecting on their experiences of the carefarm, an important theme for many participants was the place of the carefarm itself, its material and symbolic geographies and location within “nature.” The integration of the formal aspects of counseling with the outdoor environment filled with animal life helped many participants feel more comfortable discussing sensitive topics, particularly when contrasted with more traditional forms of psychotherapy.“You just feel like you're in a very safe place and it's like nobody needs to tell you to feel that way. You just, you just do. I can't describe like how that happens […] it's just like a safe haven and like a safe place and it just makes it, it changes the dynamic of how you feel while you're there.” - Jason“I say the word safe, that's really important, right? For me that was huge. It's safe. It feels safe. If you're going through bereavement, it's so important that you have that there, that that is available. I wasn't expecting that I would get all those emotions, and how strong they were. But because I was in that environment, it was okay.” - AliceThis idea of the landscape of the carefarm enabling people to “open up” and “feel safe” was a frequent theme:“I think part of creating that really safe space, where even the animals were on the same footing as the humans, was just respect for nature, whether it was plant or animal or human and I think that was actually really important.” – Nicole“The care farm, like just being in wandering around and touching the animals and being with animals. And it was we were so receptive and comfortable because we were so at ease from just existing in a world where it wasn't like, come into my office, sit down.” - Daniel“And of course the nature around you, lends to that as well, the little river, the little creek that goes by, the beautiful trees, the flowers, you know, all of that adds to it as well and it just feels like a safe place. And when you go through a trauma, like what I've been through, what you're really seeking more than anything else is safety. Cause you don't feel safe anymore. And so that provides a safe place.” - JenniferThe “affective atmosphere”  of safet“There's just so much beauty in nature. It just feels so life-sustaining, it's a nice place to be when you're not feeling, you know, when you're feeling down about anything but especially something like this […] just having beauty, you know, when you've seen something ugly, just beauty is good.” – Melissa“I was really attracted to the water and how like, you know, the water can be such a powerful metaphor too for like, especially like when dealing with grief.” - EricThe natural environment of the farm was discussed regularly by participants, particularly though the lens of grief and bereavement:“I saw this grapevine, and the grapevine was being supported by the fence, and eventually that fence would be taken away and then all the grapevines were sort of holding on to each other, that metaphor really helped me in thinking about my support network, so it was things like that, whereas before I'd have gone ‘oh it's just a grapevine you know.'” - NicoleBeing in, and with, nature on the carefarm as part of the therapeutic intervention, appeared to benefit participants and generate a sense of comfort. Some found useful metaphors from being outdoors during therapy sessions. Perhaps importantly, these metaphors were ones that participants located themselves, used to (re)narrate their ongoing relationship and ways of coping with grief:“It's also a sensory space, but the smells and visual stimulation and I think that's really important to have, to be able to feel that way in different spots of the farm. It's just in a very special sensory spot. So I can remember like certain smells or feelings and then certain areas. […] I gathered a bunch of like sand and silt and some of the vegetation from sticks, a little bit of bamboo and I gathered that in a jar and so I have that here. That's a reminder of the good times. And the sad times, but you have to embrace every single angle. I think that's important.” - EricOthers derived benefit from an embodied and sensuous encounter with the carefarm's naturescapes. These were invoked by participants through memories and material collected at the farm to provide a link back to, and ongoing engagement with, the therapeutic processes initiated at the farm:A sense of health and a sense of place are deeply and dynamically intertwined . Partici6.3.2“I have felt very alone and isolated and that there's something wrong with me and this isn't okay. And so I'm going to say from going to the carefarm, I have felt better actually. I felt significantly better.” - Alice“I was nervous coming to the carefarm because I didn't know what to expect. I was definitely stepping out of my comfort zone. But I was met with such compassion and a complete understanding of where I was, it wasn't something I had to explain, you know. It felt very, very nice to be so understood.” - SophiaParticipants frequently reported that being part of a community of people affected by grief was a beneficial facet of their carefarm experience, countering the loneliness and social isolation common in bereavement :“I have “I could not find anyone around here who really understood. I've been to three therapists now, and you know, they just, I feel like they don't quite get it.” – Laura“We felt validated and we also felt like we could tell anything and it'd be okay.” - Daniel“It's getting out there and going to a place specific to cope with grief is what really attracted me to that.” - AnnaThis sense of understanding, derived from the formation of a community fellowship of grief, was crucial for many participants in enabling them to feel safe and able to engage with the intervention itself. Indeed, many linked this sense of community to the idea of personalised care:“It's not always comfortable to talk about, you know, not everyone wants to hear about your dead baby all the time. And it just felt like a place that was comfortable and welcoming and you know, designed to help people, designed to help us.” - AndreaThe community norms, like the acceptability to talk about grief and traumatic loss, were particularly important in creating a place where people felt comfortable and accepted when discussing topics normally stigmatised by society:“That I can speak to this person and they actually understand what I'm saying as opposed to just hearing what I'm saying. […] So yes, coming to the carefarm is the first time for us that we didn't feel alienated from reality and the rest of the world.” - AmandaParticipants identified not just the ability to talk, but also the ability to be heard and understood as an important aspect of their experience. The fact that the community consisted of others who could both empathise and relate was regarded by participants as crucial:“So that was a new experience for me, being with people in person that have also lost siblings and being able to talk to them about it. […]That was the first part of realizing, you know, I'm not alone in this, you know, other people have been here in this horrible pain.” - KatyThe opportunity to meet other people affected by traumatic grief, and participating in a community, was felt to be beneficial. This, again, acted to establish the carefarm as a safe place, where discussing sensitive topics normally outside the purview of ordinary conversation was normalised:“They helped me make those connections, and also to not feel bad about feeling quite good, alongside feeling absolutely distraught. So that sort of impermanence of feeling. That it comes and it goes, and one minute you might be crying on the floor, and the next minute you might feel like going for a run, and just, allowing that, and for it to be okay. Lots of grief books just talk about how awful your life is, and how you're not able to breathe every minute, and I was like, it's like that sometimes, but not all the time, and yeah, they validated that sense in me, that feeling.” - Nicole“Prior to coming to the carefarm I was so worried I was doing grief wrong.” - SophiaCommunity members supported each other in sharing and learning ways of being-with journeys in grief:For some, the community validated their feelings. Many participants discussed fear around “doing grief the right way” or grieving “properly” that were assuaged through conversations with others. Contact with other grievers at the carefarm, and the pooling together of lived experiences, as opposed to more medicalised framings of grief, empowered participants in the legitimacy of their own emotional and affective states.6.3.3“I don't think I would've gone out there if there had not been animals. I think it would've, I think I would've just passed right over it as just another place.” - Katy“I need to be like treated gently and I'm not always getting that in the world even though people try, but I'm not always getting that. And I felt like a place that helped that, you know, has rescue animals and that it would be like that.” - LauraThe carefarm's animals, who had all been rescued from abuse or neglect, and the sense of connection to them, were significant for participants. For some, the presence of the animals acted as an important initiating factor in visiting the carefarm for support, with the animals signifying the philosophy of care built in to the intervention:“Anything you learn in the counseling, you can take it to the animals and they help you kind of digest it.” - Jennifer“I feel like the two parts are, you know, going and talking is a very intensive part where you have to dig deep and experience a lot of intense emotions and things like that. But then you get to go out and have the second part, which is you can decompress and just kind of process by walking around and being with the animals and going and being with an animal who is completely silent for the most part, but knows what it is to suffer.” - AmandaCritically, having animals present acted to create space for people to process the formal counseling aspects of the intervention:“Connecting with them on the same level, you know, they too have gone through a traumatic experience and being able to connect with them, and share that.” - AnnaSpending time with the animals encouraged reflection and processing, an outlet for solitude, and meaningful diversionary tasks. Particularly important was the carefarm's focus on caring for animals rescued from abuse and neglect. Participants discussed feeling a strong sense of connection, empathy, and relationship with the animals' traumatic histories:“I think there is something very powerful in the fact that they're thriving and alive, and some of them spunky, just living this existence. Just that they triumphed over a level of trauma and grief of their own. And that kind of provides an example of something that is possible to achieve even regardless of how bad things might appear or might be in a moment. So it just kind of gives you that hope, I guess, that things can be different.” - Melissa“I think that that definitely makes a huge impact as well. I mean, just knowing that those animals have been through hard times just the same and you can see it in their eyes, there's definitely a connection there.” - Jason“I was sitting there interacting with them and thinking how is this helping us? Cause it does feel like it's helping us. And I think, having processed it now, I think it's interacting with animals that have been traumatized and seeing that they are not the same as non-traumatized animals. There's a difference. They are more skittish and you have to really give them more time to trust you. But, they are able to still feel love. And I mean, they're able to thrive and live in this beautiful place. And I think that we hadn't really recognized that this traumatic life was forever. You just think like, oh, I'm gonna feel better in a week or six months or whatever. And I think that sort of helped me move in the direction of recognizing that we are forever changed by this event and that that's okay. We didn't choose it, but we're going to be okay. The lives that these animals lead are full of love and beauty and they didn't choose either, and they're doing okay.”- AndreaParticipants regularly recounted their encounters with animals as a meaningful and important part of the intervention. In the same way that Rose has argued that “certain kinds of landscape, encountered simultaneously as both natural, objective realities but also ‘representations’, can ‘mirror’ emotional states back to the viewer, precipitating a beneficial mentalising process” , p. 13837Both the quantitative and qualitative findings suggest that the carefarm intervention was experienced as beneficial by bereaved participants – becoming something of both a ‘therapeutic landscape’ and a ‘therapeutic community’. The mean pretest TGI-SR score of 64.09 was above the cutoff of 61 and was higher than the mean score of 53.41 in a sample bereaved by unnatural or violence causes and the mean score of 49.36 in a sample with multiple losses . These sA significant reduction in grief intensity on the TGI-SR was observed following the carefarm intervention, with a mean improvement of 14 points. At posttest the mean score had fallen below the clinical cutoff. The reduction in the percentage of participant scores above the clinical cutoff from 50% at pretest to 18.2% at posttest further underscores the improvement observed in this sample. Interestingly, the bereaved sibling group started out more highly distressed and experienced greater improvement than the bereaved parent group. The bereaved sibling group comprised a very small sample and the reason for this difference requires further study.Although the analysis revealed a significant increase in coping with grief-related distress, the newly created scale used to measure this showed poor internal consistency, with Cronbach's alpha values of .58 at pretest and .60 at posttest. The range for Cronbach's alpha encompasses 0 to 1, and values below 0.70 are generally considered unacceptable . Scales The themes identified in the qualitative analysis begin to explain the quantitative data, highlighting the value that a mixed methods approach can bring to understanding the complexities of bereavement care. The qualitative results suggest that a reduction in grief intensity can be realised through the creation of tailored, nuanced, and personalised care in bereavement support. Indeed, the qualitative results suggest that this framing can influence individuals engaging with therapeutic processes to begin with. It reiterates Hall's argument that “interventions must be tailored to the uniqueness of the person, relationship and circumstances that characterise a client at a particular point in time as they grieve a specific loss” , p. 12.where – a life is grievable. Visiting a particularized place where grief is normalised and depathologized can allow for the emergence of productive and beneficial encounters, free from societal conventions and stigmas.The opportunity to share freely, and be met with empathy and understanding, was of a critical importance. Many participants described previous negative experiences of “catch-all” services for mental health. As Butler describes, when discussing grief, “we are talking about affective responses that are highly regulated by regimes of power and sometimes subject to explicit censorship” . The carefarm, co-constituted by a “therapeutic community,” acted as a place where social conventions were cast off and participants were free to openly explore and process emotional traumas. Similarly, The results from this evaluation reflect the benefits of creating specific communities and spaces for those affected by grief to find solidarity, support, and resilience. It also suggests the value that a ‘therapeutic landscapes’ approach might bring to thinking about bereavement support. This is an area where there has been little cross-fertilisation, and we would be keen to see others working at the interdisciplinary intersections of grief and health geography further interrogate the benefits on offer here. Indeed, if it is not possible to separate experiences of health and wellbeing from the places in which they are experienced , then thRegarding the matter of animals, specifically, From a physiological perspective, hands-on encounters with animals can significantly lower physiological markers of stress, such as cortisol, heart rate, and blood pressure .2 The inImportantly, it was the involvement of animals who participants identified as having also experienced trauma that enabled the production of what was experienced as a therapeutic space and therapeutic community (of more-than-human members). This finding suggests that grieving individuals might not benefit to the same extent from more conventional care farms, where the incorporation of livestock that are ultimately destined for slaughter could be a source of emotional stress for participants .These results add to the growing body of evidence on care farming in general  and offe8This study's findings support the idea of traumatic bereavement as a complex experience affecting individuals in multiple domains, one that may require skilful and holistic community-based interventions to promote emotional and physical adjustment and well-being. Hinchliffe et al. have recently argued that making health possible “requires not just investment in biomedical remedies […] but also recognition of the relational, cultural and environmental – that is, nonpharmacological – factors that enable people to cope with life crises and transitions” , p. 8. TCarefarming, as presented here, is one example of a pathway toward therapeutic benefit for this population. For participants, this therapeutic landscape came to be understood and experienced as a restorative space and community. Interspersing focused counseling with encounters with animals and nature appeared to enhance engagement with more formal narrative therapies, as well as lead to significant reductions in distressing reactions to their own grief. Beneficial opportunities for contemplation, reflection, and depressurisation were enabled by emplacing support within the specific social and spatial contexts of the farm.This is the first empirical investigation of the effects that care farming can have on individuals affected by traumatic grief. There are important implications here for the further uptake of this style of intervention as means of supporting grieving individuals and families. Although these preliminary findings are encouraging, there are further questions to be asked, and more research that expands the evidence base for green care, both in general, and in its application to trauma and grief, is needed. Specifically, we would encourage more longitudinal studies of carefarming, more interdisciplinary investigations into both carefarming and traumatic grief, and more work that seeks to clarify the potentials, and moral complexities, of integrating animals into the delivery of healthcare interventions.10.13039/100007482Arizona State University’s Watts College of Public Service and Community Solutions. Additionally, RG's time on this study was enabled by the Wellcome Trust collaborative award on the Animal Research Nexus (205393/Z/16/Z).This study was funded in part through an internal grant from


This article seeks to understand a common and regular feature of asylum decision-making, namely, that the majority of asylum claims are rejected, mostly on the basis of non-credibility. It draws on a bottom-up, qualitative study of an administration in which asylum decision-making takes place: the Swiss Secretariat for Migration. By adopting a practice-theoretical approach to administrative work, it advocates paying attention to caseworkers’ routinised, self-evident and largely unquestioned behaviours, not only in terms of what they do, but also of what they think, feel and know. Building on Bourdieu, it introduces the concept of institutional habitus, which refers to the dispositions caseworkers develop on the job. On the basis of a specific decision-making practice termed ‘digging deep’, the article shows how these dispositions are structured and how, through the practices institutional habitus generates, these ‘structuring structures’ are continuously reaffirmed, leading to the relatively stable outcomes of administrative decision-making that can be observed from the outside. The article argues against the assumption that regularities of administrative work should be understood as the outcome of strict rule-following, top-down orders and political instrumentalism. At the same time, it challenges the individualist quality sometimes ascribed to discretionary practices in street-level bureaucracy literature and in critiques of credibility assessment practices in asylum adjudication. The first Asylum Act regulating the legal categorisation of so-called asylum seekers in Switzerland was enacted in 1981. I use the word ‘so-called’ here because the term asylum-seekers is one specific to the ‘individual protection regimes’ of the global North which consider people to be ‘individual “asylum seekers” whose “well-founded fear of persecution” has to be examined in a laborious administrative procedure before they may become “refugees” legally’ Poertner , p. 5. TAccording to Max Weber’s ideal type of bureaucracy, stability and ‘calculability of results’ are key characteristics of bureaucracy and of legal-rational authority Weber . Not onl5Wagenaar claims that ‘[m]ost administrators in a particular policy domain or government bureaucracy operate in broadly similar ways’ Wagenaar , p. 260.In recent years, several studies have addressed the issue of asylum decision-making from a bottom-up perspective, studying different asylum administrations and courts from the inside and providing insights into the everyday making of asylum decisions  trust their experienced employees is that they have been sufficiently socialised by the office, which allows them to think and act in ways which are familiar to the heads and with which they agree. With new caseworkers, in turn, they see it as their duty to form the newcomers accordingly. Trust, therefore, depends on the institutional habitus caseworkers acquire.This indicates that while habitus operates from within individuals, it is not strictly individual. Or, as Pierre Bourdieu argues, ‘[h]abitus is socialized subjectivity’ .At the end of the interview, Bernard confronted the applicant with contradictions. This again is standard and also a formal administrative requirement, since it counts as granting applicants the right to be heard , you later said that only two to three days had passed between the questioning by the authorities and the searching of the house .18Bernard, therefore, used the applicant’s recorded statements from the minutes of the interview as an ‘on-file fact’ to reason and legitimise his decision.Apart from searching for contradictions in this way, another strategy of digging deep is to test the so-called ‘substance’ of asylum narratives. What this means is that caseworkers try to find out whether asylum seekers can speak in a detailed manner about ‘events’ relevant for asylum, which is regarded as an important indicator of credibility. They do so by requesting things such as: ‘Please tell me in detail about the daily routine in prison’.Digging deep was not something I only observed experienced caseworkers do. Newcomers do it too, albeit in a more prepared way with the help of lists they have created beforehand on the basis of the documents from the asylum seeker’s casefile. Therefore, the questions we must ask are, on the one hand, how digging deep becomes a professional necessity, making newcomers more or less do it right from the start, and, on the other, how it becomes a normalised, routinised practice.freien Erzählung) asylum seekers tended to get tangled up in contradictions  will make for a strong argument, but that coming up with such arguments is dependent upon the questions she asks. She is taught to dig deep.Upon joining the office, newcomers are generally attributed a naivety that they first have to grow out of in order to not believe asylum seekers ‘too readily’. Hence, akin to what has been described for other asylum administrations as well as administrations more generally where people apply for goods and make rights claims on the state ; what they know ; and what they think and feel . All this makes the institutional habitus a useful concept for grasping administrative practice in its complexity. Furthermore, the institutional habitus considers structuring factors, such as the formal requirements of legal-administrative decision-making as well as the political, organisational and ideological environments in which the latter takes place. But the institutional habitus is not only a system of ‘structured structures’; the practices it generates also work as ‘structuring structures’  as being either too ‘soft’ or too ‘hard-line’ (see also Miaz By focusing exclusively on stabilising structures and practices, for the reasons outlined above, this article leaves its own blind spot, namely the transformative capacity that is also inherent in the institutional habitus. Researching how dispositions which structure everyday practice change over time and also how caseworkers, through their everyday practice, challenge certain structuring structures, should be at the core of future research.Beyond asylum administrations, a practice-theoretical approach with its consideration of ‘the almost unthinking actions, tacit knowledge, fleeting interactions, practical judgements, self-evident understanding and background knowledge, shared meanings, and personal feelings’ Wagenaar , p. 643