Racially and ethnically minoritised healthcare staff groups disproportionately experience and witness workplace discrimination from patients, colleagues and managers. This is visible in their under-representation at senior levels and over-representation in disciplinary proceedings and is associated with adversities such as greater depression, anxiety, somatic symptoms, low job satisfaction and sickness absence. In the UK, little progress has been made despite the implementation of measures to tackle racialised inequities in the health services. So, what is it about the health service organisational context which shapes and maintains such inequities, and what role does discrimination, bullying and harassment play? Drawing on qualitative interviews with 48 healthcare staff in London (UK), we identify how micro-level bullying, prejudice, discrimination and harassment behaviours, independently and in combination, exploit and maintain meso-level racialised hierarchies. Within teams, the high diversity–low inclusion dynamic shaped and was perpetuated by in- and outgroup inclusion and exclusion processes  often employing bullying or microaggressions. These were linked to intersecting factors, such as race, ethnicity, migration, language and religion, and could increase segregation. For racially and ethnically minoritised groups, ingroup maintenance, moving teams or leaving were also ways of coping with organisational inequities. We discuss implications for tackling racialised workplace inequities. International evidence indicates that employees from marginalised social groups experience discriminatory workplace exclusion, impacting job access, career progression, disciplinary actions and support networks . RaciallRacial and ethnic minority groups are over-represented in the NHS workforce compared with the working age general population, particularly Asian (10.7% versus 7.2%) and Black staff groups (6.5% versus 3.4%) . ApproxiIn response, the Workforce Race Equality Standard  was mandWe draw on relevant concepts from critical race theory (CRT) and from workplace diversity and inclusion literatures. CRT draws on theory, experiential knowledge and critical consciousness to argue that racism is systemic and embedded into institutional structures  and that interrelations between race, racism and power embed racialised inequalities . InterseWorkplace diversity perspectives emphasise how intergroup biases may affect the experience of minoritised staff groups . InformaFinally, the concept of workplace inclusion refers to whether individuals feel that they belong and are valued for their unique attributes and contributions . Inclusiwww.tidesstudy.com). Phase one was London-based, involving healthcare staff ) recruited from 33 of the 34 London trusts . Forty-five per cent of the NHS workforce in London identifies as a racial or ethnic minority group, over a quarter (26%) are migrant staff (non-UK born), and London consistently ranks as the worst performing region in England for many WRES indicators  study. TIDES aims to identify what is generating, perpetuating and possibly mitigating inequalities in health services . To maintain anonymity, race and ethnicity are presented as Asian, Black , White British, White other or any other ethnic group, based on self-reported Office for National Statistics classifications . We grouEthical approval was granted by the King’s College London Research Ethics Committee for Psychiatry, Nursing and Midwifery  and NHS Health Research Authority (18/HRA/0368).Forty-eight healthcare professionals representing 17 of the 33 participating trusts were recruited. Participants worked across various specialties, roles and pay bands. The majority of the sample identified as female (85%), two fifths were White British, over a quarter were from Black groups, and over a third were migrant staff. Over two fifths were student nurses/midwives, a fifth at entry-level (Band 5) posts, and just under a quarter were mid or senior nursing/midwifery staff . Of note, several student nurses also had HCA experience .Three key interacting contextual features shaped the social processes influencing the experience and perpetration of discrimination, bullying and harassment among racial and ethnic minority staff: hierarchical organisation, pressurised work environment and high diversity–low inclusion. We briefly describe how the pressurised environment and hierarchical organisation shaped the conditions for discrimination, bullying and harassment, and their implications for staff experience. We then identify how the disproportionate representation of racial and ethnic minority staff groups at lower-banded roles, alongside prejudice and stereotyping, was shaped by, maintained and perpetuated workplace inequities. Last, we examine how the workplace environment contextualised and shaped a culture of high diversity but low inclusion operating within and between hierarchical levels, increasing segregation through in- and outgroup processes see .We all know that the doctors are gods, we perceive. Then you’ve got the matron, and then you’ve got the ... and it comes down, and being an HCA [Health Care Assistant], you are the end of that.Black, non-migrant, student nurse/midwifeParticipants unanimously described the health service as hierarchical, with responsibility and risks associated with patient care and decision-making distributed by “rank.” This was primarily seniority-based, with sub-divisions within and between levels associated with role, perceived experience and expertise, duration in the team, and permanent or temporary status (e.g. agency or bank staff). At the peaks of the clinical hierarchy were senior doctors and consultants while the lowest levels were occupied by students, staff without patient-facing roles or hired by an external agency. I think the nurses get the brunt of it yeah, um. But then that has a knock-on effect on the patients because they’re so stressed and they’re like, “I haven’t got time to um deal with this situation, and then maybe the healthcare assistants will deal with it because then they’ll be like “healthcare assistants, de-escalate this situation or deal with this situation with the patient, I haven’t got time I need to be in the office, I need to do this I need to do that. Because the manager’s on my case.”Asian, non-migrant, student nurse/midwifeLower-banded positions were described as involving roles that are vital, yet also perceived as more menial, or requiring greater direct patient and family contact. Further up, qualified nurses described greater line management roles and responsibilities for clinical decision-making. Wherever you are within the hierarchy there’s always someone above you, there’s always this feeling of, erm, not being valued. Err, so for example, err, I don’t feel like nurses feel valued by the medical teams. Erm, and I think that Healthcare Support Workers don’t feel valued by nurses.White British, non-migrant, student nurse/midwifeThe social currency of this hierarchy was power, decision-making latitude, autonomy and respect. This currency afforded “value”—assigned or perceived—in relation to their status-based role, clinical opinion, contribution to the team and respect. Lack of value was particularly reported by or on behalf of the lower-banded staff but also by qualified nurses. I think because of the hierarchy culture, erm, I think it makes it quite easy for some people to, erm, that – there’s almost a lack of appreciation for what other roles do within the organisation. Erm, and I think when there is that, people tend to forget how hard others are working, or their, you know, their contribution they can make to a patient experience.White British, non-migrant, student nurse/midwifeI think because you feel one of a number, you don’t feel literally specialised in your role, you’re just doing a generic job in a way. And I think that’s probably the key point that you are, you do just feel one of many and you could be easily replaced.Other ethnic group, non-migrant, qualified nurse/midwifePerceiving lack of recognition, reward, being “one of a number” or replaceable, as well as unsupported in personal career development, exacerbated negative feelings about value, esteem and job satisfaction. A lot of our time is spent on documentation and the number of patients, that’s too many for you to give enough for every patient, like seven patients, it’s too much. […] And sometimes there will be shortage of staff or sometimes you will not actually be understaffed but we have agency staff who are working for the first time, so they need a lot of support.Black, migrant, qualified nurse/midwifeWorkplace pressures were experienced differently across hierarchical levels. Pressure was described in terms of heavy workloads and staff shortages as well as the demands and expectations of patients, families and management. This was intensified by the risks, responsibilities and possible repercussions of caring for vulnerable patients. You would not have time to go to the toilet to pass urine or you wouldn’t have time to drink. So, you would treat the, that’s what happens actually, you would treat the dehydration for the patient but as a nurse you would be dehydrated yourself.White other, migrant, qualified nurse/midwifeI think in the NHS especially, everyone sometimes expects you to work above and beyond really without having a support. So, sometimes there is an assumed perspective of, you are going to stay late and that’s just the way it is.Black, non-migrant, student nurse/midwifeSeveral HCAs, and some qualified nurses/midwives, described the physical toll of such pressures alongside a culture of expectation to put workplace demands before their own health and wellbeing. If you think about the environment, you know, you’re looking after sick patients… You’ve got… you’ve got anxious, angry parents; you’ve got, you know, all these pressures, not enough staff. There’s, you know, so much going on and you don’t get a break, etc., and I think all that adds to the heightened like environment…it can cause people, on stressful days, to really come… you know, get a bit short-tempered and stuff… Unfortunately, I think it happens more in a dysfunctional team than it does in a functional team.Asian, non-migrant, qualified nurse/midwifeCooperation and collaboration were key to performing under workplace strains but could also be compromised in stressful working environments. Participants placed great importance on being able to rely on and trust colleagues. Positive team dynamics strongly influenced staff wellbeing but was described as varying between workplaces. A good ward is one that does treat every person as equally as a person and as a valued member of the team[…]on my ward it is quite good that they’re even - you know, like, cleaning staff and kitchen staff and people who are assigned to work on the ward, who aren’t even necessarily hired by the ward, but will, kind of, count them as part of the team and invite them to ward events and stuff and talk to them and treat them like a part of the team. But there are places where, I think, those, kind of, surrounding staff are somewhere, like, ignored.White British, non-migrant, qualified nurse/midwifeSuch experiences were contextually variable and several participants highlighted examples of working with a “good team.” This not only helped them cope with workplace pressures, but also protected against hierarchical exploitations of power. I’ve seen people, like, managers calling people out publicly in front of a ward, seeming to almost embarrass them on purpose when what they did wasn’t really that bad and you think it’s just because they don’t really like that member of staff, and almost wanted to embarrass them.White British, non-migrant, qualified nurse/midwifeI had a terrible experience with someone senior in the organisation who made my life hell basically, and it didn’t end well, and I agreed to leave the organisation at the end of the year.Black, migrant, senior qualified nurse/midwifeThe workforce hierarchy and associated devaluation of lower-banded roles could extend into perceived or actual abuses of power, with adverse effects on job satisfaction, engagement and intentions to leave. If the team morale is down, people are going to be ill more, and people are going to be dreading coming into work and then actually, that probably will lead to mistakes because if you’re going to be anxious, stressed, it’s just a cycle.Other ethnic group, non-migrant, qualified nurse/midwifeAs with workplace pressures, bullying and harassment could also affect patient care and experience, directly  and indirectly (by creating a negative working environment). In my actual workplace you can tell that if somebody doesn’t like you by their difficulty or how complex your patients are on the day. It seems like, urm, if somebody doesn’t like you then whoever is allocating work, if they don’t like you, it will reflect in your workload on the day.White other, migrant, HCA/nursing associateWhile division of labour may be inherent to the hierarchy and necessary to organisational function, it could also be experienced as exploitative when requests to carry out tasks were made in ways perceived as wielding power and when staff felt unreasonable pressure was placed on them. When you went into the staff room, it was really tiny anyway but it’s only… and our nurses were all White nurses. And all the White nurses and the HCA was fine. There was another two admin staff that were, um, of White English background and they were in there. And everything was fine. But all the cleaners, any admin, like, other HCAs, the housekeeper, it felt like we weren’t allowed in there. Even when I went in to sit down, I felt like I wasn’t… Like, what am I doing here, sort of thing. Like, you guys don’t belong here in the staff room.Asian, non-migrant, student nurse/midwifeWhen nurses return back to work, like, after sickness, usually they’re given, um, a more lenient workload. Like, they, they’ll have, like, patients that have, like, less interventions or, like, less heavy you just call it. Um, and then, um, then if it’s a BME , sorry, nurse, it’s not… like, they’ll still get the same workload. Like, they wouldn’t be as sympathetic.Asian, non-migrant, student nurse/midwifeWhile workplace strains and exposure to abuses of power were described by staff across different racial or ethnic groups, the hierarchy itself was inherently racialised with lower levels disproportionately occupied by racially and ethnically minoritised groups. Participant accounts indicated that they were unduly exposed to adversities related to workplace pressures and hierarchy-based bullying and exploitation. Sometimes, like, based on, based on stereotypes, some of my colleagues, when we have healthcare assistants, they have, like, there’s a stereotype that the healthcare assistant, the Black healthcare assistant are lazy. So, there are like… I don’t want to say bully them. Because it’s a bit, like, hard. But they will be more like, can you do that, and can you do that and can you do that? So that they will not sit down that much.White other, non-migrant, qualified nurse/midwifeWe have more, I would say, Black Afro-Caribbean members in our team, actually […] we are an overstretched team, and my colleagues have made comments of, we’re not given the same … I don’t know the truth to this, but I just know we are an over-stretched team and other wards do have … probably may have better support than we do.Black, non-migrant, student nurse/midwifeImportantly, participants highlighted ways in which negative racial prejudices and stereotypes amplified such hierarchical inequities. At individual and team levels, such appraisals were systematically detrimental to the treatment, appraisal and support for racially and ethnically minoritised staff. These phenomena reinforced the division of power, “reminding” people of their position. They could also act more explicitly to maintain the racially and ethnically inequitable distribution of staff by influencing progression. Vertical progression was described as dependent on being permitted or supported to access career-enhancing training by those with greater power and therefore vulnerable to exploitation.When I think of the people who were belittling or undermining or I would say bullying as well in a way, in hindsight, I think they were the ones that felt threatened by the more, um, junior members of the team who had a little bit of spark and interest and, you know, drive to move forward and maybe they felt their positions were threatened or they were trying to put people back in their place.White other, migrant, qualified nurse/midwifeYou tend to get groups that are very close to the mangers and when they want to go and do something or do a course, they get booked on it really quickly. Or they want to get annual leave at a certain time and get first priority.Other ethnic group, non-migrant, student nurse/midwifeOn the one hand, selective exclusion and ingroup favouritism seemingly unrelated to race or ethnicity were described across participant groups; for example, linked to perceived competitive threat or nepotism. I know some people have the view of, and it is seen more when you get to higher levels of practice that BAME  members of the team don’t feel as valued, they don’t feel like they’re given promotional opportunities as much as their White or Caucasian counterparts.Black, non-migrant, student nurse/midwifeSome just anticipate because obviously they’ve heard their friend went and didn’t manage and I think we do suffer from that a lot, where we just like, it’s just not worth the hassle. We’ve seen too many people go through it and not get there, so we can’t be bothered […] if you and speak to a lot of staff now, that is the morale that a lot of them have, they’re just, ‘we know we’re never going to get this, we know what’s going to happen, so what’s the point?Black, non-migrant, senior qualified nurse/midwifeHowever, participants’ accounts also illustrated ways in which discrimination, prejudice, bullying and harassment served to maintain the hierarchy and its racialised disparities. Staff from racially and ethnically minoritised groups reported witnessing, experiencing and anticipating unfair treatment. This impacted progression directly but also indirectly, by fostering an accumulation of fatigue and resignation, deterring attempts to progress and further perpetuating the status quo. I think one of the reasons is there’s an element of self-choice. I think also they don’t believe they can do it. That’s either cultural, either their schooling, either their country of origin - we have a real problem with Filipinos [...]ín the Philippines it’s much wider apart, so women already are feeling less valued. And also, they, they are much more, um, subservient. And especially in, they have a pecking order in things. they’ll always back down if someone is more senior.White British, non-migrant, senior qualified nurse/midwifeWith student nurses I worked with one nurse and I think they saw her as a more argumentative kind of bolshie person. She was a, ugh, her accent was quite London street. So I don’t know whether they honed in on that, but they treated her very differently to the other student nurses. I don’t think it was race because everybody on that ward was a different race. So I can’t think it was because of that, um. Maybe it was just a personality clash that one. I’m not sure.White British, non-migrant, student nurse/midwifePrejudicial views and discriminatory language also underpinned opinions expressed by some (White British) participants that unequal progression was either the fault of underrepresented groups themselves or down to personality clashes rather than attributable to prejudice. Most of the people in senior posts, they are not culturally competent. They are good at their job but when you do not understand certain things, for example, observe an Afro-Caribbean person. Even when they’re having a chat. The voice is so strong. Comes across like it’s a fight. It sounds like it’s aggression even when they are having a laugh. So, that sunshine which is common in the European voices, is not usually in the African culture or the Caribbean culture except for those people that are probably born here, who’ve picked up those things. So, you know, most people, when you’re talking to them, they can’t look in your eyes because it’s a sign of respect, especially where you are their boss at work. No matter what you do, they can’t look in your eyes. But in this culture, if you can’t look into people’s eyes it looks like you’re lying. So, it takes somebody that knows much more than mashed potatoes and gravy to actually decipher this thing and be able to judge right.Black, migrant, student nurse/midwifeThese stereotypes also led to unfair professional judgements and misinterpretations about racial and ethnic staff members. This could affect progression directly by constraining support and positive appraisal. We normally split our team and you will have a Band 6 leading them and then, you will have like, four nurses, four healthcare assistants and there was one particular one that said, “I’m not going to work with her”, “Why?”, “ I don’t understand her English” […] he refused, and he was telling her in her face, “your English is ridiculous. I don’t want you to be my team leader.”White other, migrant, qualified nurse/midwifeFinally, the racialised hierarchy was also more insidiously maintained by enactments of resistance to racially minoritised staff occupying more senior positions  through microaggressions and more overt discrimination. Similar to the impact of sustained disadvantages in career progression described above, participants portrayed how chronic exposure to and battles against these everyday undermining experiences had an accumulative wearing effect on those reaching more senior levels.within hierarchical levels.Nothing I did was right. It was tough. I will have two or three invites in my inbox for meetings at different parts of the hospital. I will be humiliated in meetings. I will be undermined if I was trying to do some work, for example, with my team, I will be undermined, and it was just difficult. In hindsight I should have left, but I found in the statistics of BME staff that we put up with a lot more. So, I worked and worked and worked my bones off, and then I was accused of random stuff which was not true, and I which I knew wasn’t true and I should have fought a bit more but at the time I was broken and I just didn’t want to work there anymore. Black, migrant, senior qualified nurse/midwife. We next examine how dissimilarity on racialised dimensions  amplified in and outgroup inclusion and exclusion processes horizontally If it’s a well-known team and they’ve been working together for years, a new person coming in, they’re not … I want to say, they give you a hard time. They’re not warming. You really have to make your presence known basically […] I think they close ranks. I don’t think it’s about earning respect. I think it’s like when you’ve got a foreign object in your body and your body is fighting it until it gets used to it.Black, non-migrant, student nurse/midwifeParticipants’ accounts indicated that being the “right fit” to the prevailing workplace culture depended on the nature and composition of the workforce. For example, determined by intersecting dimensions of demographic diversity , perceived functional attributes , as well as attitudes, personality, and ways of socialising. I’m White British…and so from where I was standing it seemed like the African staff were more, like, disciplinarian and, kind of, conservative, and I felt uncomfortable with that. But I’m guessing there’s, like, cultural differences and beliefs that I don’t fully understand, that meant that that made sense for them. But then it’s, like…like, so to me having rules, like, that the patients have to stand in a line and not speak before they can get their medication seems, like, pointless and it’s, like, well this isn’t a…this isn’t the Army.White British, non-migrant, student nurse/midwifeHowever, these varying criteria of “fit” were all vulnerable to prejudice and stereotypes and demonstrated lack of inclusion, such as intolerance of cultural differences in working styles. White British, non-migrant, student nurse/midwifeAfter working for a day or two, my manager came in and said that he doesn’t want Blacks there.Black, migrant, qualified nurse/midwifeShe [a colleague] was working on a ward with a lot of, um Black African nurses and she said um, she said that she didn’t like working with them and I asked why, and she said that they had a lot of attitude and then she used the phrase “Black attitudes” and she said similar sort of things to what the patients would say. She said they were bolshie. That they’re superior, um. They were non-communicative and I think she even said something like, that they were ignorant […] she was one person and it’s not a view that I’ve noticed in a lot of other people, especially not that strongly, but I would say there was this attitude of, um, yeah towards, towards West Indian or African nurses. I had one placement where it had a really positive approach with diversity ‘cos like, there was just, like, within that team you had…there was, like, people from the Caribbean, from the Middle East, like, White British people, people from Mauritius and, like, it would be…people were just, kind of, like, share what they…their, like, kind of, what they knew and their cultural backgrounds they, kind of, would use that in their work, like, if someone had a patient who was, like, Nigerian or something there would always be someone on the team who would be, like, “Oh, the reason he’s behaving that way, that’s not a mental health issue that’s, like, a cultural thing cause in our culture we do this and this.” So that team it, kind of, having a mixture worked really well.White British, non-migrant, student nurse/midwifeDepictions of diversity in relation to race, ethnicity and migration status varied according to the team composition and the attributes of the person perceiving it. For instance, in line with information and decision-making perspectives, workplaces comprising staff from multiple different racial or ethnic groups were perceived by many as positive working environments beneficial to patient care. I think it’s more, if you’ve only got one or two different groups then sometimes you end up with, like, everyone, kind of, joins sides with one group or the other and you, kind of, end up with, like, you know, like, there’s, sort of, one social group that’s, like, all Black and one that’s all White and it’s, like, kind of, a bit odd.White British, non-migrant, student nurse/midwifeIn contrast, diversity was portrayed more negatively in relation to inter-ethnic group tensions or hierarchies and where there was a clear divide between racial and ethnic groups, particularly if this involved an uneven split or an inversion of “traditionally” minority and majority groups. This type of segregation maintained horizontal hierarchies defined by in- and outgroups by two distinct processes, “insidious dismissal of outsiders” and “resistance and coping.”There are wards that, you know, places can get quite, kind of, clicky and get quite a specific culture and, you know, people get their friends on and, you know, bring people in and then if someone doesn’t like a member of the team, they’ll just treat them badly and then they will eventually leave, so that means you’re just creating this kind of, you know, survival of, like, them people where, you know, everyone else will leave if they’re not the, kind of, people that they want until they, you know, have created a team of almost entirely the people who work and are like them and then it’s horrible for anyone who goes and isn’t exactly like that.White British, non-migrant, qualified nurse/midwifeSome certain kind of people, they will support more their people. Like, one place I used to work had a lot of Filipinos. So, some of the band sixes were Filipinos. And then when in the shift there were more Filipinos, they will support them more compared to how much they will support us.White other, non-migrant, qualified nurse/midwifeThey’d go out after work with the White English, like, they’d include her in as well. Like, they’d go out for drinks whatever. But the Black student nurse was never… wouldn’t be invited.Asian, non-migrant, student nurse/midwifeInsidious racially motivated inclusion and exclusion processes were described in which people were influenced to leave  or supported to stay within a particular workplace. This contrasted with the more explicit bullying and discriminatory behaviours which maintained vertical hierarchy. The way they were towards, eh, a, a Black, um, guy that was the admin. And he actually left a couple of weeks before I left ‘cause he was the one who mentioned it first. But I didn’t say it like that. He was like, do you notice they’re, like, racist on this ward? I don’t want to work here. I’m going to be… I’m going to a different ward in a couple of weeks.Asian, non-migrant, student nurse/midwifeAs a newly qualified staff when I go to my, whatever job I’m going to go for, if I don’t like the culture there, I’ll just move on and move on and move on and move on until I get a, find a team and culture where I do feel as I fit in.Asian, non-migrant, student nurse/midwifeThe way you pronounce things will be different from all people, but there are some among them, no matter how you say it, they wouldn’t understand you […] And the way they will behave or squeeze their face, you know it will have an impact on you, it will be down on you […] And in most cases when you come um, across people from the minority, actually, you enjoy more because they all understand you.Black, migrant, qualified nurse/midwifeParticipants expressed how racial and ethnic minority groups acted to redefine “insider” and “outsider” status, or to alter their working environment by moving away from unhealthy or toxic environments or leaving the workplace altogether. When I’m searching, I would like to go to a place that I find only White people, European, that’s fine. European, that’s fine. Usually, it’s African Black or Caribbean. And even if you search on YouTube and you see, like, women harassing Muslim women or like, most, 90 percent they are Black women.Other ethnic group, migrant, qualified nurse/midwifeReflecting examples of ethnic hierarchies and inter-ethnic group tensions, this segregation process also occurred between different racial and ethnic groups. Now in my place I haven’t experienced it [discrimination] per se, in my place, yeah because um, we are in team of the majority are from the minority group. So, even our manager is also from the minority group. And the other members, and the cooperation out there, and then how they support each other. Yeah. You wouldn’t experience it per se or do it, but let me put it this way, we are all the same. A few are um White, about two or three. The rest are from a minority. And the manager is also from the minority and welcomes everybody. We tell jokes and we talk to each other as if we are all equals.Black, migrant, senior qualified nurseBlack African nurses especially in in-patient settings. I don’t find this is the case in community settings. I find it’s the only case in in um in-patient settings. In the community it’s very different it’s very diverse in terms of age, in terms of race and ethnicity. Um but in-patient settings […] because we, it’s a 24-hour care, so you pass on the responsibility to the next team, and I think you have that security as a nurse - you feel like the job is safer? So when you leave your shift, you’ve done your handover to the next team, who are your friends or people that you’ve worked with for years and years and years. They will always have your back.Asian, non-migrant, student nurse/midwifeIn contrast, segregation may reflect a preference for working in an environment with other minority groups, experiencing it as more supportive than those dominated by a White majority group. I feel like, umm, the African nurses were kind of, were friends and they’d a-, also all been there a very long time, like they were all kind of friends in like a kind of clique and they like, I don’t know, they knew how to talk to each other, which sometimes to me felt like they were arguing but they weren’t they were just talking about something and then they’d laugh about it afterwards.White British, non-migrant, qualified nurse/midwifeWhite participants often reported feeling excluded from these “cliques,” reflecting discomfort or fragility associated with the unfamiliar experience of exclusion as a minority. Like in the in the nursing office, sometimes they speak their own language, they - they just have this culture, I can’t explain it- they just have this culture about them? And. It just feels really uncomfortable and it feels like, you wouldn’t be supported, and I feel that’s one of the reasons why the nurses and the HCAs have been there for decades.Asian, non-migrant, student nurse/midwifePotentially in opposition to the double jeopardy faced by migrant racial and ethnic minority groups, using shared (foreign) language could be a particularly powerful tool to invert predominant insider and outsider statuses and to subvert prevailing inequities in social support. We know that particularly BME staff in particular won’t speak up. They are very reticent about speaking up about things that happened to them which is a concern[....] because people feared the consequences for themselves, they fear being called a snitch.Black, migrant, senior qualified nurse/midwifeOne of the midwives I was speaking to, she’s African, Black African, and she said to me that… I said to her, like, you need to make a complaint because there’s a couple of things that, that were being said about her at work. About her being difficult and stuff. And she goes, “oh, yeah, oh, yeah, and then I escalate it and you think that that, that stigma’s going to be taken away from me or get worse?”White British, non-migrant, qualified nurse/midwifeThe features of workplace context described above also inhibited staff members capacity to challenge discrimination, bullying or harassment in turn serving to help maintain the status quo. Respondents described difficulties in challenging or reporting adverse experiences due to concerns about impact on career progression, fears of upsetting team dynamics and “rocking the boat,” and of being labelled a “snitch” or “troublemaker.” Such fears were reported by staff across racial and ethnic groups though for minoritised (particularly Black) staff, these concerns were weighted more heavily as they were compounded by the effects of prejudice and discrimination. Within racially and ethnically diverse healthcare settings, we qualitatively identified how the racialised organisational culture was shaped by and perpetuated micro-level inter-staff experiences of discrimination, bullying and harassment. Despite high diversity, inherently racialised hierarchical inequities reflected and influenced lack of inclusion. Bullying, prejudice, discrimination and harassment were used to exploit and maintain the racialised hierarchy, and were exacerbated in pressurised environments. Moreover, race, ethnicity, migration status, language and religion were among salient intersecting markers of similarity/dissimilarity. These processes stifled inclusion and often employed bullying or microaggressions. For racial and ethnic minority staff, ingroup maintenance could also be a way of coping with workplace bullying, discrimination and harassment, though the workplace context served to limit staff capacity to challenge and report unfair treatment.Participant accounts portrayed a highly racially and ethnically diverse but often un-inclusive workplace, and evidence suggests that where inclusivity is low, high levels of workforce diversity have been associated with racial tensions, conflict, as well as reduced group identity and employee cohesion, performance and satisfaction . MannionGenerally protective for mental and physical health, coping refers to the behavioural and cognitive processes used to manage demands appraised to exceed an individual’s resources . Coping Our findings strongly suggest that tackling bullying, discrimination and harassment and redressing occupational, and subsequent health inequities, necessitates disrupting accepted norms of organisational culture shaping staff interactions. This is challenging because of the complexity of the health service and the diversity of its workforce, intensity of staff-to-staff interactions and task interdependence, unequal distribution of power and disproportionate representation of racially minoritised staff at lower-banded levels. Further, this is set in the context of pervasive prejudice and stereotyping, and the inhibiting effects of anticipated discrimination and “racial battle fatigue” . This isInterventions tackling discrimination among healthcare staff have been reviewed , findingOur study supports existing strategic-level recommendations to promote systems-change, including sustained strategies transparently linked to senior-level accountability targeting organisational, workplace inter- and intra-personal levels . This neData were collected just prior to the COVID-19 pandemic in the UK, the killing of George Floyd and subsequent Black Lives Matter activity. Our findings therefore exemplify the marginalisation, victimisation and racialised inequities prevailing prior to these events . As withStereotyped and prejudicial views of racial and ethnic minority and migrant staff inhibit inclusion and underpin intergroup biases which limit workgroup performance, staff experience and ultimately staff health. Efforts to promote work/life balance, proactively foster inclusion by equitably valuing and esteeming difference in roles, attributes, knowledge and perspectives, and proactively levelling-up opportunities for career progression are structurally key for staff retention and improved health outcomes. To reduce disparities, interventions require systemic shifts to organisational culture through directly addressing overt prejudice, stereotypes and microaggressions and their impact on health.


These myths backed by misleading statements of “statistical significance” can cause far-reaching harm to patients and doctors who might not understand the pitfalls of specious statistical testing.The recent paper in JAMA alleging that frozen embryo transfer causes twice the risk of childhood cancer in the offspring is an excellent example of the erroneous use of statistical tests (and the misinterpretation of  On December 10, 2019, the very prestigious and high impact Journal of American Medical Association (JAMA) published a study titled “Association Between Fertility Treatment and Cancer Risk in Children,” authored by Hargreave et al. ”, “mainly due to an increased risk of leukemia . In the primary analysis, 90 sets of comparisons were made with diagnosis of infertility, utilization of fertility drugs, and assisted reproductive technology (ART), using fertile women as the reference group. This gave rise to three “statistically significant” hazard ratio (HR) estimates based on a 2-sided The media have been in a tumult in response to this study. Internet articles with alarming titles, such as “Freezing embryos doubles risk of IVF kids developing childhood cancer”; “Babies born from IVF using frozen embryos may be more than TWICE as likely to get childhood cancer”; “Children born from frozen eggs twice as likely to develop cancer”; have appeared with frightening alarm. In the methodology and interpretation of this study, there are profound errors which could devastate infertility patients. Many physicians, although skeptical, are not criticizing the paper’s profoundly flawed conclusions, but are merely saying that “the effect is too small to worry about”. This study by Hargreave et al. represents a prime example of the widespread confusion in statistical testing and scientific inference. We wish to address these confusions specifically in regard to this paper, but also for a vast array of similarly misleading papers which do great harm in the name of “statistical significance” which is not actually significant.p = 0.003. The idea of using alphabetic letters in the surname to predict pregnancy is so absurd and implausible that all readers will know it cannot be valid despite the glittering p value. That is why we did the “study”: to emphasize that p values should not be interpreted in isolation. By studying groupings of any three letters in the surnames of these patients (as well as single and two letter groups), we had some 3000 combinations for analysis and were virtually certain to find p = 0.003 somewhere. However, in real life, the same abuse of statistics is often far less obvious to the statistically naïve, as in the Hargreave study. It is the uncorrected use of multiple tests for “statistical significance” which is the problem.Hargreave et al. made 90 sets of comparisons in their primary analyses. This is a classic example of “data mining,” also known as “data fishing,” “data dredging,” or “data snooping” . To undep-value is the probability under a specified statistical model that a statistical summary of the data would be equal to or more extreme than its observed value” [p value refers to the probability that the difference would have been at least as large as what they observed, if the same study were repeated across many different populations and settings, and if all the underlying assumptions were correct. These include the assumptions underpinning the study design, data collection, statistical models, and inference, as well as our assumption that the p value was not selected for presentation because it was < 0.05, and that the study design had been reported with completeness and transparency [p value is a parameter that reflects the compatibility between the statistical models and the observed data. A small p value could have arisen from inappropriate model specification, flaws in the assumptions during study design, or study bias. In other words, a “significant” p value is neither necessary nor sufficient for us to determine that there is a “significant” effect, because the p value itself tells nothing about the validity of the study methods. Likewise, with regard to 95% CIs, the 95% refers only to how often 95% CIs computed from very many studies would contain the true value if all the assumptions used to compute the intervals were correct [p value and the 95% CI rely heavily on the distribution of the study data.A review of the principles of probability and statistical testing will aid the understanding of this issue. According to the American Statistical Association, “a d value” . Using tsparency . The p v correct , 5. The if all the assumptions underlying the statistical model hold. However, if we do observe a value that is beyond this interval, we will know that either the null hypothesis does not hold, meaning there is an association between FET and childhood cancer, or at least one underlying assumption is violated, or we have run into this finding purely by chance (type 1 error).The concept of a 95% CI can be further illustrated with a slightly different approach. If we assume that there is no association between FET and childhood cancers, the null hypothesis will be HR = 1. Imagine us drawing a study sample from the whole population of children and then estimating the HR. We would replace the children back to the population, and repeat the same random sample drawing and HR estimation process 100 times, generating 100 distinct HR estimates, and 100 distinct 95% CIs. If the null hypothesis of “HR = 1” holds, we will be confident that these 95% CIs will cover “1” 95% of the time, and the probability of seeing anything beyond this interval will be less than 5%, p < 0.05”, originated from an arbitrary decision driven by tradition and convenience; it may be used to screen for potentially valid assumptions, but should not be equated with scientific conclusions [p value into ‘significant’ and ‘nonsignificant’ is an especially pernicious scientific practice” [p < 0.05” is the question “Is the p actually <0.05?” If we estimate the HRs for 90 sets of comparisons, then the probability that all our estimates will cover the “true” values under the null hypothesis, at the significance level (α) of 0.05 for each comparison, will be (0.95)90 = 0.0099. Therefore, assuming there is no model misspecification or bias, the probability of us finding something “significant” just by chance (type 1 error), and hence rejecting at least one null hypothesis, will be 1–0.0099 = 0.99. In other words, in the study by Hargreave et al., they would have found some “statistically significant” HR 99% of the time, even if the finding would be as senseless as predicting pregnancy from the letters in the patients’ names.“Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty” . Uncertaclusions . Thereforactice” . Compounall the planned comparisons lumped together, then for each individual comparison, the α should have been 0.05/90 = 0.00056, instead of 0.05. With such corrections, what was reported in the Hargreave study would not have been statistically significant based on the α defined a priori, unless they could show a p value less than 0.00056.Several statistical methods have been proposed to reduce the risk of type 1 error in the case of multiple comparisons, assuming the study is otherwise free of bias. Among these methods is the Bonferroni correction, which is a more conservative approach that minimizes spurious findings by chance. Using the Hargreave study as an example, if we were to set the significance level such that we may reject a null hypothesis by chance 5% of the time with α for each interim analysis (α spending), in order to control the overall type 1 error rate [Similarly, in clinical trials, various methods have been developed to restrict the ror rate . Withoutp < 0.05, deceiving not only the media and the lay public, but also many physicians. In their Statistical Methods section, the authors state “Because no adjustment was made for multiple comparisons, the findings should be considered exploratory given the large number of treatment subgroups that are being compared.” But if readers did not examine the supplementary tables carefully, it would have gone unrecognized that the vague statement of “the large number of subgroups” actually meant 90 tests for statistical significance. With so many comparisons, it was inevitable that some “statistically significant” but probably meaningless effect would be found. Responsible publishing dictates candid disclosure of the number of comparisons attempted, transparent report of the actual statistical adjustment made to address multiple comparison, honest recognition of the uncertainty in statistical findings, modest reporting of study results and their “practical” significance, and open acknowledgement of all other study limitations.It is thus obvious that even if there is no other bias, the combination of a “let’s go hunting” approach and the lack of proper statistical correction in the Hargreave study have given rise to the dubious association and causation have persisted to date. In 1965, Bradford Hill addressed these issues in his seminal paper, “The Environment and Disease: Association or Causation?” [Strength of the association between the exposure and the outcome, as measured by statistical parameters, such as relative risk (RR), odds ratio (OR), incidence rate ratio (IRR), HR, etc. Bradford Hill considers this to be the most important feature of a causal association. For example, the causal association between female age and infertility can be demonstrated with the drastic decrease in the live baby rate per oocyte of 26% in patients younger than 35, to 1% of those who are older than 42 .properly performed in different scenarios and at different times. For example, the association between age and infertility was also supported by national ART surveillance data in the U.S. and Australia/New Zealand [Consistency across studies  Zealand , 13, amoIn 1950, Richard Doll and Austin Bradford Hill published the famous study on the causal relationship between smoking and lung cancer, which has been the prime example of causal inference in epidemiological studies [sation?” . In thisneither of these HRs was statistically significant. It is a blatant error to say that this represents “an elevated HR”. In other words, while Hargreave et al. misleadingly claimed that at least one out of four previous studies supported their FET-childhood cancer theory, in fact none of them provided such support. We find it surprising that such a bold misinterpretation went unnoticed during the review and publication process for JAMA.Recognizing the importance of the consistency criterion, Hargreave et al. quoted four previous studies examining the association between FET and childhood cancer in the Discussion section. Three of these studies did not support such association. They then wrote: “one study reported an elevated HR for any type of cancer  and another study reported an elevated HR for hepatoblastoma .” Note that the 95% CIs for both HRs crossed 1, and therefore Scientific inference is a far broader concept than statistical inference… A major focus of scientific inference can be viewed as the pursuit of significant sameness, meaning replicable and empirically generalizable results among phenomena. Regrettably, the obsession with users of statistical inference to report significant differences in data sets actively thwarts cumulative knowledge development.” [(3)M. tuberculosis causing tuberculosis in humans, and human chorionic gonadotropin or luteinizing hormone (LH) triggering ovulation.Specificity of the association, such as (4)Temporality, meaning the cause should precede the outcome in time. For example, in a spontaneous ovarian cycle, the LH surge precedes ovulation.(5)Dose-response relationship. This is corroborated by the continuous decline in women’s fecundity as they age –13.(6)Biological plausibility, as is concept of chromosome 21 nondisjunction leading to Down syndrome, has been plausible for the medical community.The importance of the consistency feature was recently reiterated by Hubbard, Haig, and Parsa: “opment.” .(3)SpeciFertility and Sterility in 2013 [While we acknowledge that this may be limited by the biological knowledge of the day and can evolve over time, so far, there is no good evidence supporting an association between FET and childhood cancer. In the Introduction section of the Hargreave study, there was only one study that seemed to support the biological plausibility of this hypothesis: “It has been suggested that the use of fertility treatment increases the risk of cancer in children, possibly through epigenetic changes brought on by the use of fertility drugs, ART, or both.” However, this turned out to be a paper Hargreave et al. published in  in 2013 . The lac(7)Coherence with generally known facts. For example, the chromosome 21 nondisjunction phenomenon observed in genetic studies is coherent with the higher incidence of Down syndrome among children born to older women. Likewise, in the Hargreave study, the coherence criterion may be met if epidemiological data do show an increased risk of childhood cancer associated with FET, and bench studies also reveal certain pathophysiological pathways leading from FET to oncogenesis.(8)Supporting evidence in clinical trials or experiments. For example, the causal association between the transfer of multiple embryos and multiple pregnancies is supported by randomized controlled trial data, where transferring two embryos in younger women led to drastically more multiple births (33.1% versus 0.8%), without increasing the overall live birth rates with a comparable magnitude .(9)Analogy found in other relevant areas. Understanding the association between maternal age and trisomy 21, we would be ready to accept similar associations between age and other forms of aneuploidy.The effort of Hargreave et al. to rationalize their claims did not stop there. In the Discussion section, they went on to state that estrogen was “an established carcinogen”, and progesterone was “reasonably anticipated to be a human carcinogen”. Such statements are dangerously misleading, and in fact are also contradictory to their findings that fertility drugs were not associated with childhood cancer.No formal tests of significance can answer those questions. Such tests can, and should, remind us of the effects that the play of chance can create, and they will instruct us in the likely magnitude of those effects. Beyond that they contribute nothing to the ‘proof’ of our hypothesis.”“As one may see from the list above, statistical significance is necessary, yet insufficient, to define causation. The comments by Bradford Hill in 1965 on this matter were as relevant then as they are today :“No formA well-contemplated conceptual framework and an a priori hypothesis are central to the validity of any epidemiological study. The conceptual framework is represented by all the epidemiological processes that can give rise to an observed association, and all the biological processes that can make such associations reasonable. In constructing the conceptual framework, one must consider confounding, selection bias, mediation, effect modification, generalizability, and the contrast between clinical versus statistical significance.if factors that contribute to the non-exchangeability have been adequately accounted for. In the Hargreave study, the only covariate included in the main models was the year of birth categorized in 5-year intervals. To suggest that this represents an adequate control of confounding is to assert that the comparison groups were indifferent in all the other aspects. That is manifestly implausible.Confounding is one of the fundamental issues in observational studies, and it is essentially an issue of non-exchangeability. In order to prove an association between FET and childhood cancer, ideally, we would hope to have two groups of children who are identical (or “exchangeable”) in every feature, except for the utilization of FET in the index pregnancies. However, as common sense tells us, this is impossible in human studies. The next to ideal method to address confounding is randomization, which is often unfeasible due to logistical or ethical reasons. Statistical models that adjust for confounders may mitigate some of the bias, Since the comparisons were made between women who conceived through FET versus those who conceived spontaneously, the two groups differed not only in the usage of FET, but also distributions of parental age, diagnosis of infertility, use of fertility drugs and ART, embryo cryopreservation, thawing, and transfer. Even within women who were diagnosed with infertility, the etiologies could have been male factor, pelvic factor, anovulation, endometriosis, uterine anomalies, just to name a few. The severity of infertility also differed to some extent among women who eventually conceived spontaneously, versus those who required ART to conceive. Couples undergoing ART are, on average, older than those who have no fertility problems. This is demonstrated in the Hargreave study where 50.7% of the children of the fertile couples were born to women aged under 30, and 14.8% were born to the oldest of the three age groups (> 34). This is very different from the ART group, where only 27.4% of the children were born to women younger than 30. The distribution of paternal ages showed a similar pattern. Since advanced parental age is associated with pediatric cancers , 18, it Along the same line, the children’s age should have been studied as a confounder with more granularity. First, it has been shown that the proportional distributions of pediatric cancer types vary by age . For exaIn addition, donor  ART cycles should have been accounted for, as the genetic predisposition of children born from these cycles is strongly related to that of the donors. The age of the donor should be used in the regression analysis, but was not used in the Hargreave study. Was this appropriate? According to the Centers for Disease Control and Prevention, 9% of the ART cycles in the United States (U.S.) in 2016 intended to use eggs from a donor . In 2014The indications and techniques for FET from 1996 to 2012 have evolved remarkably . During It is obvious at this point that spanning from FET to childhood cancer is a conceptual framework that is far too complex for the simple appraisal Hargreave et al. presented. Systematic evaluation of confounding, mediation, selection bias, and even effect modification may be facilitated with directed acyclic graphs (DAG) . A samplstatistically correlated with the exposure or the outcome. Consider the association between diabetes and endometrial cancer as an example. A systematic review of the subject knowledge will reveal age, parity, obesity, smoking, polycystic ovary syndrome, etc., as confounders [Despite these utter biases that are impossible to rationalize, Hargreave et al. attempted to justify their analyses with purely statistical claims. “Only year of birth… was included in further analyses because none of the other factors changed the risk estimate by more than 10%.” The magic “10%” cutoff is completely empirical, and may play a role when there is no other way to determine if certain factors are indeed confounders. However, researchers must first identify confounders by carefully reviewing the subject knowledge. Confounders thus found must be accounted for in statistical modeling, even if they do not seem to be founders , which mE-value to prove that statistically the effect of residual confounding was negligible. Introduced in 2017 [E-value is a relatively new concept even for clinical epidemiologists, let alone the statistically naïve. Expressed on the risk ratio scale, E-value represents the minimal strength of association the unmeasured confounder needs to have with the exposure and the outcome, in order to explain away the observed association. If the E-value is high, it means it will be difficult to explain away the observed association, which is therefore robust. But the validity of the E-value is conditional on adjustments for the known confounders, which was not done. It is therefore not a panacea for the gross confounding bias in the Hargreave study.Hargreave et al. did perform a post hoc calculation of the  in 2017 , E-valuenot synonymous with confounding, which, as we discussed above, is the dissimilarity among comparison groups. Therefore, by performing sensitivity analyses as such, Hargreave et al. did not address the critical issue of confounding in their primary analyses. Sensitivity analyses that repeat the same errors will not validate the fundamentally spurious findings in the primary analyses.To support their primary findings, Hargreave et al. also performed a sensitivity analysis by adding sex, birth weight, gestational length, and multiplicity as the “intermediate factors”, and changed the reference group to women who conceived with “fertility assistance”. This led to another 99 sets of comparisons and similar findings as those in the primary analyses. “Intermediate factors”, also known as “mediators”, are factors that may have mediated the causal pathway leading from the exposure to the outcome Fig. . For examost of the confounding, such that even though we have not randomized the study participants (and therefore non-exchangeability will persist to certain extent), we will at least be comparing grapefruits to oranges. Otherwise, we could end up comparing dinosaurs to oranges, in which case, the conclusion will not be valid no matter how impressive the p value may be. As Bradford Hill critiqued, test of significance should only be a guide, rather than a rule, and one should never let the glitter of the p “divert attention from the inadequacies of the fare” [As the saying goes, “We don’t want to compare apples to oranges.” In observational studies, properly designed regression models may address he fare” .p value is largely driven by the unknown true value of the population, which we attempt to estimate, as well as the distribution of study data. A large sample size increases the precision of the estimate and the likelihood of this estimate being statistically significant. This is a common phenomenon in large registry studies, as in the study by Hargreave et al., where 14 cancer cases among 3356 children born from FET pregnancies actually gave rise to statistically significant estimates. But such a small number of cases could not have given rise to a p < 0.05, had the authors not taken a “shotgun” approach of multiple comparisons and ignored the confounders. Further, when the baseline childhood cancer risk was 17.5 per 100,000 person-years, a “two-fold” increase in such risk actually translated into 26.9 additional cancer cases per 100,000 person-years. The number needed to harm, which is the inverse of the absolute risk difference, was 3717.5  person-years. Even if the analysis in the original study was valid, hundreds or thousands of FETs would have to be avoided in order to “prevent” one case of childhood cancer. The small magnitude of this dubious “risk” is in contrast to the numerous benefits of FET, such as unprecedented flexibility in ART treatment, drastic reduction in the risk of OHSS and multiple pregnancies, the opportunity for embryo banking, and preimplantation genetic testing [dubious statistical significance has not translated into any probable clinical significance. But aside from the obvious lack of “clinical significance,” in fact the Hargreave study also lacks the “statistical significance” that it claims.As discussed above, the  testing , 25. The25% of the cycles were started with the intent to freeze all resulting eggs or embryos for potential future use [With data misinterpretation, a speciously sound-appearing methodology may produce misleading conclusions with devastating results to society . While tture use . A spurinot that statistical methods can be used to prove almost anything, but rather that gross abuses may deeply mislead the non-statistician.” [The Scandal of Poor Medical Research, Douglas Altman observed, “Huge sums of money are spent annually on research that is seriously flawed through the use of inappropriate designs, unrepresentative samples, small samples, incorrect methods of analysis, and faulty interpretation… Responsible medical journals invest considerable effort in getting papers refereed by statisticians… Unfortunately, many journals use little or no statistical refereeing - bad papers are easy to publish.” [The fallacies, errors, and confusions Austin Bradford Hill started to tackle 70 years ago remain just as outrageous today. As two of the coauthors stated 29 years ago, “It is ublish.” .scientific inference with medical school, residency, and fellowship curriculums.Basic statistical literacy is essential in preventing myths, just as vaccines are essential in preventing the spread of infectious diseases. We all play a role in rising to the challenge. As Altman said, journal editors and reviewers should contribute their due diligence as the “fire fighters” against flawed research studies, balancing potentially important findings with the knowledge of study limitations, statistical uncertainty, need for confirmation studies, and the media’s tendency to omit subtle technical caveats. Clinicians, the second gatekeepers of research information, “need not be experts in statistics, but they should understand the principles of sound methods of research.” . To meetIt is the responsibility of leading medical journals, such as JAMA, to evaluate and reject statistically unfounded studies, even though the specious conclusions they generate may draw headlines and elevate the “impact factor.” When even high impact clinical journals fail us, we must be vigilant in not accepting unsound conclusions.


Laboratory indicator test results in electronic health records have been applied to many clinical big data analysis. However, it is quite common that the same laboratory examination item  is presented using different names in Chinese due to the translation problem and the habit problem of various hospitals, which results in distortion of analysis results.A framework with a recall model and a binary classification model is proposed, which could reduce the alignment scale and improve the accuracy of lab indicator normalization. To reduce alignment scale, tf-idf is used for candidate selection. To assure the accuracy of output, we utilize enhanced sequential inference model for binary classification. And active learning is applied with a selection strategy which is proposed for reducing annotation cost.Since our indicator standardization method mainly focuses on Chinese indicator inconsistency, we perform our experiment on Shanghai Hospital Development Center and select clinical data from 8 hospitals. The method achieves a F1-score 92.08This work demonstrates that the structure we proposed can be effectively applied to lab indicator normalization. And active learning is also suitable for this task for cost reduction. Such a method is also valuable in data cleaning, data mining, text extracting and entity alignment. The second one is a habit problem. Different hospitals or different doctors prefer different names or have their own statements, such as “”, “” and “”. Since there exists no such a synonym knowledge base (KB), EHRs associated with these synonymous names will be lost, resulting in distortion of the analysis results. Therefore, it is necessary to design an automatic method for lab indicators standardization.Electronic health records (EHRs) have been applied to many clinical data analysis, such as prognostic analysis and decision support. In EHRs, laboratory indicator test results are considered to be important factors. For example, “farction . HoweverAn effective way to solve the standardization of lab indicators problem is entity alignment. There are two main tasks related to the entity alignment: instance matching and entity linking. Instance matching  aligns sAn effective framework is proposed to normalize the lab indicators, which combines a recall model and a binary classification model. The purpose of the recall model is to reduce alignment scale, a candidate set contains standard indicators is selected for each non-standard indicators. After this step, candidate-standard indicator pairs can be generated by a binary classification model through an enhanced sequential inference model(ESIM) based on the name and abbreviation of indicators. Experimental results of the proposed structure show that it achieves an F1-score of 92.08Active learning is utilized for reducing annotation cost. A new selection strategy is proposed and is compared with shannon entropy, least confidence and a random baseline. Experiments show that the our strategy performs better than random baseline and could outperform the same result which is trained on full data with only 43A detailed case study on heart failure clinic analysis is conducted on the sub-dataset from the dataset of a regional healthcare platform called Shanghai Hospital Development Center (SHDC). The result shows that our proposed method is practical in data cleaning, data mining, text extracting and entity alignment.In this paper, a framework is used for indicator normalization. Including a recall model and a binary classification model, and then is evaluated by a case study on heart failure, which extends our previous work . The maiFigure n and a represent name and abbreviations, respectively.The original data we collected from hospital is a set of indicators with name and abbreviation. The aim of data pre-processing is to generate a standard indicator set. Though terminology standard like LOINC has been published early, it has not been widely used in clinical practice. In Zhang’s work , lab indWe collect a standard indicator set containing 1970 items through the first step. For a single non-standard indicator, it would be costly if it is compared with each item in standard indicator set due to the huge amount of standard indicator list. Similar challenges occur in recommending system and query engine, where a light recall model is often used to shrink the scale of candidate set, then a complicated model is applied for further processing. We use the similar idea in our task. To improve the efficiency of the whole system, a recall model is used to compute the similarity between non-standard and standard indicators. Each indicator has two attributes: name and abbreviation value. So two candidate set v whose length is equal to the length of the vocabulary. The corresponding value of each word in v is the tf-idf value of the word. Then the similarity between two indicators is represented by the doc product of their corresponding vector. The abbreviation of some indicator is missing during practice, so the length of candidate set for each non-standard indicator is unequal, range from The recall model selects a certain amount of candidates in a short time with a high recall rate. In this task, the textual similarity between the names and abbreviations of non-standard indicators and standard indicators is very high. So we have tried four classical text matching model: edit-distance, bow(bag of words), bm-25 and tf-idf. Experiments show that tf-idf works best on the test dataset. Tf-idf is described as follow. For a document set mentclass2pt{minim” and abbreviation “ph” will be transferred to “”, which avoiding training two model targeted to name and abbreviation separately. We note the non-standard indicator and the standard indicator as query and hypothesis, represented as a and b respectively.Considering that the recall model has sharply decreased the amount of candidates for each non-standard indicator, we apply a binary classification model to judge the relationship between a non-standard indicator and its candidate standard indicators. Before training the model, we combine the name and abbreviation of each indicator. For example, an indicator with name “We define the binary classification as a natural language inference task. Though pretrained language model such as Bert  achievesESIM consists of four parts. Input encoding utilize BiLSTM to represent each word with its context. Local inference modeling apply attention mechanism to interact query with hypothesis. Inference composition is proposed to compose the output of previous layer into a fixed length vector. Finally, a prediction layer put the vector into a multilayer perceptron classifier for classification.a and b, BiLSTM is used to learn a representation of a word and its context, notated as i on the input sequence, which is same with For input Local inference modelling apply attention mechanism to capture the local reference between query and hypothesis, which is formulated in Eqs. Before the final prediction, a composition layer is proposed to compose the previous enhanced representation. Then a pooling layer is applied to convert the resulting vector obtained above to a fixed-length vector. Finally, the fixed-length vector is put into a multilayer perceptron classifier taken softmax as activation function. Cross-entropy is used for training. The strategy used in active learning has a great importance to the final result. The main idea to select samples is based on the uncertainty about the prediction of the model. A variety of strategies have been proposed to calculate model prediction uncertainty, such as least confidence, margin sampling and shannon sampling, where margin sampling is same as least confidence in binary classification. Least confidence considers uncertainty as the difference of 1 and the maximum probability of the prediction. For example, in binary classification, the probability of the model prediction being 0 and 1 is ith non-standard indicator paired with jth candidate in The data trained in binary classification should be annotated by experienced experts with high cost. Active learning aims to use fewer samples to train without degrading much performance. which select a certain amount of samples by some strategies and sent them to professional experts for annotating. The whole process is iterative and the pseudo code is given in Algorithm 1. The dataset is collected from Shanghai Hospital Development Center. Among 38 hospitals, we choose 8 hospitals whose data are diverse and rich enough in synonymous indicators and types for our experiment. We extract two attributes of indicator from the dataset, namely name and abbreviation. Then a list of standard indicator names are selected and each standard indicator was annotated with some synonymous indicators by experts. The number of extracted indicators is 12,298, and the standard indicator list has 1970 items. As for the settings in our experiments, the recall number on indicator name edit distance: a model measures the number of operations to transform one string to another.bow(bag of words): a basic model to represent text into vector for similarity calculation.bm-25: a baseline model in information retrieval.The result is shown in Tables We compare tf-idf with three other baselines:For binary classification, we compare ESIM with Zhang’s methods  and anotThe results are shown in Table We empirically compare three active learning strategy mentioned above with a random baseline. The result is shown in Fig. We have experimentally evaluated our proposed standardization method in the section of Results. In this section, we further give a detailed case study on a real application, namely heart failure clinic analysis, to show the necessity of lab indicator standardization.(Serum sodium)” .In order to better understand the performance of the proposed method, we manually analyze an indicator related to heart failure called “” is a standard indicator selected by experts. We take its non-standard synonym “” as example. The first step is using recall model to select a candidate set. Then the binary classification model is used to get the standardization result. Tables Since “”, in which contains many semantically similar indicators. The classification model is able to find the correct standard indicator and exclude all non-synonyms: “”, “”, “”, etc. Which is similar to the name of sodium ion in Chinese.The proposed method is able to produce presentable standardization results. It can be seen that the recall model successfully find a candidate set related to “” and “” appear in the candidate set. This shouldn’t be a surprise since the recall model is based on character level and the indicators with overlapping characters have a certain chance of being clustered. Besides, such irrelevant negative samples will make the distribution of label in binary classification uneven, which brings challenges for further training process. It is remarkable that the recall method has included the groundtruth, regrettably at the expense of misjudging some irrelevant non-synonyms.Nevertheless, our analysis also points out that the proposed standardization method has limitations. For candidate selection step, in Table ” (serum sodium) in the SHDC dataset, which is obviously missing since there are more than 100 thousand inpatients in the research group and “serum sodium” is a common indicator in Shanghai hospitals. Observing the records in the dataset, we find that “serum sodium” is also described as “” (sodium), “” (ionized sodium). We then search the records using names supplemented by our algorithm and 150,000 records are associated.The role of lab indicator standardization is then introduced. In academic research about association between use of statins and outcomes in heart failure, the lab indicator “serum sodium” is considered as a relevant factor. Before indicator standardization, only 50,094 records can be found out by searching name “So far, we can see that lab indicator standardization is helpful in the following procedures: data cleaning, data mining, text extracting and entity alignment. For data cleaning, standardization is its main step. For data mining and text extracting, more synonymous indicators standardized by our method, more records can be searched. For entity alignment, lab indicator standardization can be considered as an entity alignment problem.The heart failure research is conducted on the sub-dataset from SHDC. The SHDC dataset contains EHRs from 2012 to 2016. Data specifications in different hospitals are all different so that expressions of the same item may be distinct. For example, one hospital uses lab indicators’ English names for abbreviations while another hospital uses inner numeric codes of the hospital. With the dataset, we use our algorithm to clean the data and preprocess the data according to the research goal and the guidance of specialist doctor for varies diseases. Figure The first step is mainly about our proposed method. A generic SHDC lab indicator KB is independent to special diseases or academic subjects and can be applied to all tasks based on the same dataset. As the generic KB can be used in lots of tasks, it can prevent duplication of data preprocessing and data cleaning while offering an easy-to-use standard for follow-up special disease researches. Specifically, we input lab indicator reports of the SHDC dataset into our algorithm and output candidate-standard indicator pairs into an easy-to-use KB. Each synonymous indicator is linked to its standard indicator and standard indicators are organized in the tree structure which is easy to view and mange.The second step is the procedure of data selection. According to the requirement of a research, several related indicators are picked out into specific indicator KB. In this case, commonly used indicators in heart failure are put into a heart failure indicator KB. Figure ” (sodium) is mapped to “” (serum sodium) and “”  is mapped to its standard indicator “” (aspartate aminotransferase).The last step is the post-structure step. Each synonymous indicator is recognized using the heart failure indicator KB constructed above , and it In this paper, we propose an effective recall-and-classification structure based on active learning to standardize the lab indicators in SHDC. To decline the alignment scale, we test several classic text matching methods and finally utilize tf-idf to recall a candidate set for non-standard indicators. And to assure the accuracy of output, an enhanced sequential inference model(ESIM) is performed for binary classification. Experimental results show that ESIM can achieve an F1-score of 92.08As for future work, our proposed method includes synonyms at the expense of adding some irrelevant items in the case study, which suggests the possible research avenues that remain open. In the future, we also plan to optimize our proposed method to construct a lab indicator KB covering the whole data of Shanghai Hospital Development Center, and apply it to other clinic analyses.


Vaping is a relatively new practice, and therefore its symbolic meanings and social practices are yet to be fully understood, especially within Australia where the practice is strictly regulated. This study aimed to examine vapers motivations for use, reinforcing influences, and association with the vaper subculture.Working from a constructivist epistemology and a symbolic interaction framework, in-depth interviews were conducted with a purposive sample of 37 current (89%) and former (11%) adult vapers, 70% male, mean age of 32.5. Data was analysed via thematic analysis.Vapers largely started vaping to quit smoking and underwent common experiences during their initiation phase. Subsequently, vapers tended to adopt one of two dominant identities, that of the ‘cloud chaser’ or the ‘substitute’, which some users moved between during different stages of their vaping career. The social and symbolic meaning of e-cigarettes and vaping varied and involved concepts of harm reduction, addiction, pleasure, stigma and community, and for some, connection to the vaper subculture.Understanding the complexities of vaping, and the nuanced differences of ‘cloud chasers’ and ‘substitute’ vapers may have important implications for health communication, research and policy. E-cigarette users within this sample were not a homogeneous group and differed in their motivations for use, association with the vaper subculture and relationship with the vape community. These findings provide new insights into the socialisation process and subsequent identity adoption of vapers within the unique regulatory environment of Western Australia. Since entering the American market in 2007 , e-cigarE-cigarettes were originally developed as an alternative form of nicotine delivery and potential smoking cessation device . HoweverSmokers cite numerous reasons for starting vaping, these include: to ease nicotine cravings and withdrawal symptoms; to quit smoking or avoid relapse; to use e-cigarettes where smoking is prohibited; reduce cost; and the belief that e-cigarettes are less harmful than tobacco –11. HoweResearch from Europe has explored e-cigarette user’s motivations, self-identity as vapers and involvement in vaping subcultures. Farrimond  identifiVaping is a relatively new practice, and therefore its symbolic meanings and social practices are yet to be fully understood. However, it appears that through the uptake of vaping, personal and collective identities have been established and a vaping subculture has emerged , 15, 20.In Australia, liquid nicotine is classified as a ‘Schedule 7-Dangerous Poison’ under the Federal Poisons Standard . Hence, Since the early 1990s, Governments in Australia have enacted progressive comprehensive legislation to reduce the impact of tobacco , and as Vaping devices are referred to by users and scholars by a multitude of terms, including e-cigarette, ENDS (electronic nicotine delivery device), vape and mod. In this paper, the term e-cigarette is used to represent all of the various consumer products available.Symbolic interactionism is a micro-level sociological theory providing the theoretical framework underpinning this study . SymboliParticipants were purposively sampled for maximum variation in demographic characteristics  - a ranking derived from the economic and social conditions of people and households within an area ) within A multipronged approach to recruit participants was utilised. Recruitment flyers and posts were placed on four online vaping forums ; seven subreddits on Reddit; and 30 closed vaping Facebook groups. The lead author created personal accounts on each of the forums and social media. Facebook groups were accessed by requesting permission to enter the group as a researcher to recruit study participants. Vape retail stores, online and bricks and mortar, within the GCCSA of Perth were contacted via email, social media and webpage submission forms. Snowball sampling was also utilised.Interested individuals were invited to contact the lead author via email or telephone to express their interest in participating and receive further details about the project and what their participation entailed. After reading the participant information statement and providing informed consent, interviews were arranged at convenient safe public locations . Interviews were conducted in English by the lead author who has experience in qualitative data collection. Interviews lasted on average 49 min (range 25–86 min) and were audio-recorded with participant consent. Participants were provided with an AUD$25 gift voucher at completion of the interview as an honorarium for their time.A semi-structured interview guide was developed to allow flexibly and adaptability within each interview , and pilAll interviews except one (file corrupt) were transcribed verbatim by an independent professional transcription service and checked for accuracy by the lead author. The detailed notes taken by the lead author during and after interviews were sent via email to the participant the following day to review for accuracy and validation. Amendments from the participant were returned via email. Transcripts and interpretations were not provided to participants for respondent validation. Interview transcripts and detailed notes were anonymised and imported into NVivo (v12) to facilitate data organisation and linkage. The lead author conducted all coding, allowing for a single researcher to be immersed in both the data collection and analysis, thereby ensuring that the coding frame adequately described the intentions and content of the interviews .The analytical process followed the steps proposed by Braun and Clarke  for themStrategies to enhance the rigour of the research included the use of a codebook to provide structure and agreement about code definitions, constructs, and themes; in-depth rich description of the research methods through field notes, personal reflections and analytic memos ; and disDemographic and behavioural data were analysed using descriptive statistics SPSS v26). The reporting of this study is guided by the consolidated criteria for reporting qualitative research (COREQ) checklist   and former vapers  . Thirty-two participants (87%) were current or former cigarette smokers and five (13%) were vaping despite having never been a regular smoker. Five participants were dual users of tobacco and e-cigarettes. Former  and current  smokers had been using tobacco on average for 14 years . In comparison, participants had been vaping on average for 2.4 years .Thirty-seven interviews were conducted with current , device functionality , nicotine concentration, and the users smoking history, if any. Those who were smokers described this experience as being similar to that of their first drag of a cigarette, whereas non-smokers had difficulty articulating their experience as they had nothing to compare it to.Participants were predominantly introduced to e-cigarettes through work colleagues, friends, and during their time abroad in locations where e-cigarettes were more readily available (e.g. the UK). For the majority of participants, this introduction marked the first occasion they had seen or heard about e-cigarettes. Subsequently, participants asked if they could “As a non-smoker, it was really hard for me to grasp the concept. Everyone was trying to explain it to me like it's either like a cigarette or a bong. I was like, ‘I don't know what you're talking about.’ They were like, ‘mouth-to-lung, or direct-to-lung’ and I still can't even understand the concept. If I had to explain to you what I do, I don't know what I do. I press the trigger, I breathe it in, it comes out.” Karis, cloud chaser  had no nicotine in it, as is Australian rules. I ended up actually putting my own [nicotine] in it because it was just, too smooth basically, you could taste it, but you couldn't feel it which is what I want, I want to feel it [throat hit].” Brody, substitute , on this thing that's firing. The first few times that I did it, I don't think I was doing it properly because I was firing it for a really short amount of time, taking like really small puffs. Then when I did do it properly, I was like, oh this is what it's meant to feel like and taste like." Karina, substitute  because whilst I'm using that [vapoursier] I have no intention to have a cigarette. That's how passionately I don't want to smoke again, but I'm not prepared to risk it at this time, but who knows? … Do you know what, I often think I probably do need to cut back a little bit and I think, well I'm not smoking?” Ursula, cloud chaser  really is a means to an end [nicotine/smoking cessation].” Ella, substitute , I do all my own, I build all the things, I use all the rebuildable stuff. So yeah, it has become a bit of a hobby, which is why I think it appeals to certain people, because it has that sort of community aspect where it becomes like a hobby … they all sort of get together…” Wade, cloud chaser  close to the expert stage. An advanced user, I'd say. When you start using mechanical mods, that's when you're an advanced user.” Zadie, cloud chaser  those companies.” Zadie, cloud chaser . We've got it down to a really fine art. There's names of [tor]nados that you can do like specialised ones and stuff like the DNA, the double, oh it’s crazy. So we go all out. Like you've got to wet the table, make it stick, and you've got to layer it. So we do layer upon layer upon layer of smoke. No one’s allowed to breathe. If you breathe, you're dead.” Clara, cloud chaser  just very childish, … and because it's still quite a small community, everything's a personal attack against someone else. Like, if so and so were to have a sale and then he'd think that it was a direct attack on him. It is very clicky and very immature a lot of the time, I don't know why. I don't really bother doing much with Facebook groups because that’s just where it all is. When it's in-store and stuff and it's all very professional, everyone's very eager to help, it’s just everyone seems to become a keyboard warrior online.” Wade, cloud chaser  in retail because I have an ethical code where I can't sell something that I don't believe in and I believe 100% in the industry of vaping and what their motives are. I think it's good. It is entirely good and all the people that I've met who also promote it and stand behind it have good intentions, and their sole drive is to see people get well and stop smoking. We want to make smoking history just as much as the non-smokers. That's the thing … almost all vapers are reformed smokers…” Quade, cloud chaser [d chaser The Australian NDSHS has been regularly conducted since 1985, and first provided limited data about e-cigarette use in 2013. Data from the most NDSHS  reports Thirty-two vapers in this sample were committed smokers for several years and five participants were dual users of tobacco and e-cigarettes. The primary reason for initiating vaping was to quit smoking, citing less than optimal successes with other TGA (Therapeutics Goods AdministrationThe majority of vapers in this sample were former smokers, however, several respondents had taken up the practice despite having never smoked. Understanding how vapers ‘make sense’ of their health practices  is requiParticipants within this study generally exhibited limited knowledge of the potential health effects of e-cigarettes. However, as reported by vapers abroad , 53, theParticipants mostly described positive reactions from friends and family to their e-cigarette use, particularly when their goal was to abstain from smoking. In this sample of vapers, few had close friends who vaped and therefore sought camaraderie through online fora and vape retail stores. As found in other qualitative inquires , the notAlthough tobacco smoking is legal in Australia, the decline in prevalence combined with the denormalisation of smoking and societal aversion has fated the behaviour to be predominantly relocated to the fringes of society and viewed as a deviant and marginalised behaviour . For somTwo approaches to vaping emerged from our data, that of the ‘cloud chaser’ and the ‘substitute’. Vapers within this sample displayed similar subcultural elements and practices to those reported in the international literature examining the motivations of e-cigarette users, identity formation and involvement in the vaping subculture , 15 whicSupported by Farrimond  and McQuThe assessment that e-cigarettes are a tool to manage nicotine addiction among ‘substitute’ vapers may explain why these users did not strongly identify with, or actively rejected connection with the social identity of vaping, and enjoyment did not play a substantive role in their use and maintenance . ResearcE-cigarettes are both technically complex devices, which novice users may find difficult to spontaneously start, and a non-medical consumer product, which has resulted in the need for many aspiring users to look to other vapers as their experts, building a vast and international social network of shared knowledge and identity . A commoThese findings were gathered from a small purposive sample within a specific geographical context and time, and therefore may not be generalisable to the broader vaping community or e-cigarette users abroad due to Australia’s regulatory environment, absence of mass media advertising and lack of Government endorsement as a smoking cessation aid . HoweverFew studies have explored vapers motivations for use, reinforcing influences, and association with the vaper subculture, especially within the unique regulatory context of Australia. We found that our sample of vapers largely started vaping to quit smoking and underwent common experiences during their initiation phase. Subsequently, vapers tended to adopt one of two vaper identities, that of the ‘cloud chaser’ or the ‘substitute’, which some users moved between during different stages of their vaping career. The social and symbolic meaning of e-cigarettes and vaping were diverse. ‘Cloud chasers’ connected with the vaper subculture in varying degrees and involved concepts of pleasure, community and performance. However, the aesthetic and performance part of the subculture, in particular, had little appeal to ‘substitute’ vapers who largely viewed their use of e-cigarettes as a means to quit smoking, and enjoyment did not play a substantive role in their use. Understanding the complexities of vaping, and the nuanced differences of ‘cloud chasers’ and ‘substitute’ vapers may have important implications for health communication, research and policy. Our findings add to the understanding of the varying motives for use and provide new insights into the socialisation process and subsequent identity adoption of Western Australian vapers.Additional file 1. Data collection guide. The data collection guide includes the information recorded about the interview, participant’s demographic and behavioural information and the interview guide.Additional file 2. COREQ checklist. A checklist of items that should be included in reports of qualitative research.