As digital media becomes more central to the lives of adolescents, it also becomes increasingly relevant for their sexual communication. Sexting as an important image-based digital medium provides opportunities for self-determined digital communication, but also carries specific risks for boundary violations. Accordingly, sexting is understood either as an everyday, or as risky and deviant behavior among adolescents. In the affectedness of boundary violations gender plays an important role. However, it is still unclear to what extent digital sexual communication restores stereotypical gender roles and restrictive sexuality norms or, alternatively, enables new spaces of possibility. In this sense, current research points to a desideratum regarding adolescents’ orientations toward sexting as a practice between spaces of possibility and boundary violations. This paper discusses the possibilities, but also the risks, of intimate digital communication among adolescents. The main question is, how adolescents themselves perceive sexting practices and how they position themselves between both spaces for possibility and for the exchange of unwanted sexual content. For this purpose, orientations toward normalities and gender of students are reconstructed. To answer these questions, twelve single-sex, group discussions were carried out with students aged 16 and 17 at five different secondary schools in northern Germany. A total of 20 boys and 22 girls took part. The group discussions were structured by a narrative generating guideline. The analysis draws its methodology from the Documentary Method, regarding implicit and explicit forms of knowledge and discourse. It results in a typology of three types with different orientations. The study shows, that most of the students consider sexting to be a risky practice; only one type shows normality in the use of sexting. At the same time, some of the young people are interested in experimenting with image-based intimate digital communication. Further, gender differences in use and affectedness are also documented. In this way, orientations toward gender stereotypes “favor” both the attribution of responsibility to girls, and overlook the responsibility of students who perpetrated the boundary violation. The orientations of adolescents should be taken more into account in research as well as in educational programs for the prevention of sexual violence. Sexuality is a culturally and historically mutable concept that has transformed markedly over the past 100 years – and continues to do so. Representations of sexuality, norms, values, and practices are adaptable, and are closely connected to specific historical and cultural contexts. From early childhood onward, the process of sexual socialization allows individual attitudes, positions and structures of desire to form in engagement with social sexual norms and values. These individual sexual “scripts” both describe sexual identity and shape individual sexual experiences and actions. Especially during adolescence, the formation of sexual identity is characterized by specific dynamics.In recent decades, the environments of children, adolescents and adults have rapidly digitalized. In particular for adolescents, digital media represent an important part of their lived reality, and are also used to shape sexual activity and communication online . CorrespOne particular phenomenon that has gained increasing attention in public, educational science and pedagogical practice in recent years is the so-called practice of sexting: that is, the “private exchange of self-produced sexual images via cell phone or the internet” , 1. AlmoThis paper explores the orientations toward the exchange of intimate images and boundary-violating communication through digital media in a school setting. The basis is a qualitative research project, which analyzes the orientations – defined by Bohnsack as patterns of meaning that suggest shared forms of communicable knowledge  – on sexting among school students. The study will address how students interpret and interact with intimate, image-based content, including implications for sexual boundary violations online. Further, the study describes how adolescents position themselves in the field of tension between spaces for possibility and spaces for sexual boundary violation. In the course of the liberalization of society and increasing sexual self-determination in Germany, the former sexual morality has been more and more replaced by a negotiation morality . Now theThis paper sets out by reviewing recent research on sexual communication and boundary violations through digital media that draws on the phenomenon of sexting and focuses specifically on the aspect of gender , before detailing the study on which this paper is based . Based on group discussions, see Section “Results” offers insights into the orientation of students in Germany. The paper then summarizes the findings in a conclusion (see section “Discussion”).Given that sexuality is a socially constituted practice, it is important to consider the specific historical and cultural contexts that inform adolescents’ sexual development. Within this cultural context, sexuality takes shape via highly individual, socially and historically mutable sexual norms and values . In the During the process of sexual socialization , adolescFrom the moment the smartphone entered the life of adolescents as an omnipresent device, it has enabled low-threshold, uncomplicated access to the internet and thereby also to sexual communication and interactions online . DigitalA Spanish study on 2,356 high school students documents that being involved in digital sexual communication “does not appear to generate a negative impact among those involved, in a short term.” Adolescents who feel the need to be popular may consider sharing and disseminating sexual content as a strategy to be accepted among their peers , 8. MoreSexting involves sending intimate sexually explicit messages, photos, or videos via smartphones and computers, and describes a mode of connecting sexuality and digital media . As one 1. Public and academic debates around adolescent sexuality in digital contexts have long revolved primarily around questions of risk. This tendency is most prevalent in debates where the repercussions of adolescent use of pornography are discussed  mean that online sexual boundary violations are comparatively more severe than in “analog” spaces  – althouUnderstandings of gender have significant influence on the development of sexual identities, and on the risk of sexual boundary violations. Sexuality and gender identity are developed above all during adolescence. In data on participation in sexting, the non-consensual dissemination of sexually intimate content and the emotional distress this can cause, gender factors are often discussed. In the main, it has been established that there is very little difference in participation levels between boys and girls when it comes to sexting see . At the In this sense, this discussion generally plays out within the dominant order of the gender binary, insofar as the terms “boys” and “girls” create two homogenous groups that reinforce asymmetric vulnerabilities in relation to sexual boundary violations through the representation of gender. A recent Canadian study, however, suggests that the central question is not so much the representation of gender, but the attitudes toward gender stereotypes . AccordiAs documented by this brief review of recent research, there are a number of quantitative surveys available on participation in sexting, on non-consensual behavior and the role of gender with regard to sexting. However, This paper addresses the ways that adolescent school students deal with intimate digital visual content and offensive communication within digital media. Additionally, it questions how adolescents position themselves in the field of tension between spaces of possibility and sexual boundary violations. A particular focus lies in the reconstruction of orientations toward normalities and gender.The data gathered by the following empirical reconstruction of school students’ orientations is drawn from the research project ‘SaferSexting – Perspectives of School Students,’ conducted by the BMBF  between 2018 and 2021 within the funding stream ‘Research on sexualized violence against children and adolescents in educational contexts’. The research project looks at sexting and its largely uninvestigated connections to sexuality, non-consensual sexual conduct, digital media, and the school context.2. Two of them were grammar schools (dt. Gymnasium) and three (more applied) comprehensive schools (dt. Gemeinschaftsschule), so the entire range of the German secondary school system is represented in the sample. Nine of these discussions are taken up in the following analysis. Group discussions with students were gender homogeneous, due to the fact that the current research findings outlined above indicates gendered differences both in relation to sexual boundary violations and sexual communication via digital images. Thus the 12 groups each contained 2 to 6 participants, who were interviewed in a single-sex setting. Five group discussions were held with male participants, seven with female. A total of 42 students  took part  In addition to immanent questions to maintain the narrative exmanent – like ‘‘can you tell more about it’’ or ‘‘do you remember other situations’’ – follow-up questions were asked about sexuality and digital media, sharing of sexts and sexual boundary violation . The duration of the group discussions ranged between 45 and 106 mins. These were transcribed in accordance with the TiQ  guidelinesThe analysis of the group discussions draws its methodology from the Documentary Method, in which both implicit and explicit forms of knowledge and discourse are analyzed . This alThe documentary analysis carried out through the relational construction of types yielded a typology with three different orientations: “The Experimenters,” who uncritically view and use sexting as an everyday form of sexual communication; “The Reflexive-Criticals,” who likewise consider sexting to be normal, but are critical of violations; and “The Disapprovers,” who reject all forms of sexual digital communication. Each type contains three of the nine group discussions examined. Group discussions with both girls and boys are represented in each type. In what follows, each type will be described in terms of its orientations toward norms and toward gender, drawing on exemplary excerpts from the group discussions.Gamblers and Girls’ Night differ only minimally, in terms of how they position themselves in relation to adolescent ‘normality’ through their actions.The first type practices sexting in an experimental fashion. This involves both self-determined and non-consensual forms. The groups Gamblers describe sending sexts as an everyday practice among adolescents that involves both girls and boys.The students belonging to the group B5: It’s kind of like, you hear about it from other people, if someone sends something around, like in the year level or whatever.B3: A dick pic?B5: Yeah or like @(.)@ also of aI: ⌊A what please?B3: A dick pic.B5: Also from the girls’ side. ah you just kind of hear about it and then of course people talk about it, let’s say.[…]from your girlfriend. and of course ah @you’d then be a gentleman and ah you wouldn’t forward something like that or show it ah to other friends. (1) and (1) I hope after the relationship ends it’d stay that way, that ah it stays anonymous and private, kind of.B3: Yes. um (1 s) if you have a girlfriend then it’s also, I’d say, pretty normal that you’d get um these sorts of pictures The students frame the practice of sending sexts as being commonplace among fellow students in their year group – a practice that is both widespread and openly acknowledged (the students “hear about it”). Established practices include sending “dick pics” and receiving images “from the girls’ side.” Here, laughter points toward the shared fun of being involved. In particular within heterosexual relationships, for boys, receiving images from their partner is described as being “relatively normal,” and confidentiality in this matter is framed as a question of “honor.” The question of whether sexting takes place with consent is not further discussed by the students, implicitly suggesting that the potential for sexual misconduct or violation here appears to be an irrelevant detail.Girls’ Night are characterized by normative assumptions about sexting as a “normal” sexual form of communication among adolescents.Similarly, the orientations of the group G2: Well I would say, because really a lot of people do it, maybe not nudes, but like (.) kind of (.) revealing, ah, images. Sure (.) um on Snapchat. […] and I’d say that I don’t really think it’s bad either, because I think it’s pretty much normal, that you um kind of try it out at some point and also maybe that um you want to get an opinion about yourself, if you’re not really sure. like um if your body or I don’t know it’s like-. that’s a feeling, I think, that lots of girls have, that they need confirmation. so um I don’t mean that in a negative way, that’s I think totally normal, that you um also um just want to know what other people think about you. and that’s why, I think that’s why it’s normal, every girl or also every boy, at some point.There is no doubt that the students in this group present sexting for girls as a legitimate space of possibility, of positive affirmation during adolescence. Sexting is a way of attaining validation about one’s appearance and one’s body, and represents a means of dealing with insecurities about one’s own attractiveness. This option is less tied to the recipient of the sext, meaning to exclusively erotic relationships; rather, it represents a legitimate possibility for the sender of the sext to find out “what other people think about you,” on the sender’s own terms. Here, self-recognition and the recognition of others takes place through reciprocal exchange. This process of verification is explicitly framed as being a “normal” adolescent need.The term “normal” here relate not only to self-determined sexting practices, but also to the non-consensual receipt of sexually explicit images. One female student, for example, recounts having received unsolicited and sexually explicit images. She points out that she believes “plenty of people have received images like that before” – in doing so framing non-consensual sexual communication as a normal activity. The shared laughter in response to her statement documents agreement; the experience of this non-consensual practice is shared by others in the discussion. Speaking through her laughter, the student explains her strategy in dealing with such experiences: “I’m personally not interested in that stuff and I usually just delete the chat or block him or whatever, yeah.” Through this statement, the experience of shame, of having one’s boundaries violated, is not entirely dismissed, but rather is accepted as a part of adolescent experience that can be countered through simple technical steps. Sexual boundary violations are thereby assumed to be a self-explanatory part of sexting, and are accepted as the inevitable negative price to be paid for the personal self-affirmation otherwise afforded by the practice.Girls’ Night as well as for the group Gamblers, in which this orientation is particularly prevalent.The type “The Experimenters” reflect traditional ideas about constructions of gender, positioning themselves affirmatively within practices that differentiate between two genders. Girls and boys are allocated different roles in the representation of gender, in the sense that boys are sexually active, while girls are positioned on a fine line between the demand to be attractive and sexual passivity. This holds true for the group B5: Generally though I’d say that the girls cop more than the boysB6: Cop more, what do you mean by that?B3: ⌊What do you mean?sure, I mean, I know a lot of guys who um would secretly, like, record sex with a girl.B5: ⌊B3: ⌊@Oh God@.B5: And uhyeah, totally.B6: ⌊Ah, that’s what you mean. […]B2: I think it’s often like um, like for example um, […] for guys who don’t post them themselves, but they get shared around a lot anyway um, that it really doesn’t matter, um whether they wanted that or not. but the guy involved, the guy, (.) he isn’t hated on. mostly other guys just say: “Oh, nice one, nice work.” And the girls get hated on. then it’s mostly likeB6: ⌊Yeah, like: “Oh, what a slut.” And for the guy: “Oh, what a cool guy.”This orientation points toward gendered differences based on an asymmetric value system involving sexual double standards. In the example detailed, although the boy involved both creates and spreads non-consensual sexual content, he receives confirmation as a “cool guy,” while people develop a “negative impression” of the girl, and she is “hated on” as a “slut.” The boy is completely exonerated of responsibility, while the girl – the victim of the situation – is condemned. In this sense, a judgment is made via a double standard . The boyAlthough the participants appear to be aware of the asymmetries in the effects of these gendered conceptions (as demonstrated by shared agreement), this does not lead them to take a critical position. Participants neither express criticism of the non-consensual recording of sexual content, nor do they address the non-consensual publication of the recordings. Rather, they maintain an apparently neutral narrative voice, meaning that their own position remains ‘suspended’. At the same time, the narrative itself is not at all neutral. Laughter and obvious delight signal a collectively shared agreement that not only supports but amplifies the labeling of the boy involved as a “cool guy,” and the girl as a “slut.” Bindesbøl Holm Johansen et al. argue, that “non-consensual sharing acts as a form of visual gossip to maintain social bonds and gendered recognition” and that this has “gendered implications as it rests on and reproduces gendered values” , 1029. TGirls’ Night addresses the non-consensual forwarding of sexually explicit images of girls among boys, they state that “it’s not really that bad when people talk about it.” Thus, practices of sexual misconduct or violation among boys are viewed as “talk” that might be “interesting,” and are thereby marked as being integral for adolescent sexuality. The discrepancy in how boys and girls are affected by these activities is accepted as a given, and is not interrogated.The girls in type A also orient themselves according to gender stereotypes. Along these lines, when the group Negotiators of Responsibility and Feminists. Type B positions itself in opposition to dominant societal notions of normativity and gender  well mostly on Snapchat and then um he kind of wrote to her and then he like straight away sent her um photos of himself topless and wrote really sexual things to her. I mean really, really G?: ⌊°Shit°.G4: and that and um after that I think she then also blocked him and um you also see stuff like that maybe also sometimes on InstagramG5: ⌊Mhm. ((agreement))G4: And you can also report it. the thing is that Instagram doesn’t usually acknowledge it. No, um notG?: ⌊Nah.not OK, there are actually pages, they’re like from some people and they um promote child pornography […]G4: n-, really. So I, when I see something like that and report it, because I don’t I find it, I mean, it’s Feminists point toward the option of blocking as a simple technical step to prevent further unwanted contact. Unlike the group Girls’ Night, however, the female students here collectively position themselves against an unwanted attempt to initiate contact, including against the sending of images of penises. The group additionally criticize the lack of responsibility of operators of social media services, which react inadequately to reports of sexual misconduct. As the narrative on the topic of social media services continues, they criticize the fact that such platforms do not react when users advertise abusive images of children, so they are confronted with unwanted sexts. Unlike type A, they thereby criticize the described “normalities” as a culture of non-consensual sexual communication experienced by (girl) adolescents, and actively reject it.The female students describe an exchange on a social media platform in which an unspecified male individual begins by sending revealing images with “sexual” and “pornographic” texts. The girl refuses this attempt to make contact. Instead of accepting her refusal, the person intensifies the level of boundary violation, “spamming” the girl with images of his penis. Here, too, the female students of the group Negotiators of Responsibility discuss a fake account that a fellow student set up on a social media platform. The male student used the account to pretend to be a woman and convince a fellow student to send images of his penis, before showing these images to the class. This practice is rejected by the Negotiators of Responsibility.In a similar way, the group B1: Because it just went too far. um, because (.) writing to someone with a fake account and then getting him to um (.) put his umB3: ⌊Private parts.B1: his penis um online. that’s actually- that’s really not cool. that’s really seriously messed up, actually.B3: It’s more than just messed up, it’sB1: ⌊and then to um show this image to others. and laugh about it.B5: Yeah.B1: I didn’t find it funny at all.The students’ strong disagreement with this transgressive practice increases throughout the duration of the conversation in the sense that a boundary is marked out (“it just went too far”). Their shared, normalized judgment of being “really not cool” intensifies to “seriously messed up, actually.” Not only do they condemn the deception of the victim, but they also mark the act of sharing the image within the class – and the resulting ridicule – as an intensification of the boundary violation, and they distance themselves from this act .Type B, then, also see the practice of sexting as a daily component of peer interactions. In other parts of the group discussion, they also detail their own, self-determined experiences with sexting. This type, however, reports above all on the non-consensual sharing and publishing of intimate images, suggesting that a self-determined form of sexual communication online remains an unrealized dream to be fought for. Sexual boundary violations are identified as such, and are rejected without responsibility being relativized. Type B attribute responsibility for violatory sexting practices on a number of levels, but they do not attribute guilt to victims (thereby avoiding both victim-blaming and slut-shaming). This means that they not only discuss the acts of fellow students who perpetrate misconduct and boundary violations, but also address their own behavior, thereby reflecting on possibilities for acting. While boys critically reflect upon their own reactions in retrospect, girls also relate their criticism to further contexts of socialization.Negotiators of Responsibility reflect on their own actions, the Feminists criticize sexualized power relations and proclaim self-determination.The type “The Reflexive-Criticals” maintains a critical distance to contemporary gendered sexual norms and taboos . The valG3: (.) But also this thing um with women and how they should present themselves, I just had this big discussion with my sister about it, because she’s taking photos for her Bachelor and um in none of my outfits in the photos was I wearing a bra. and you could see my nipples. and that’s a discussion for sure, in a porno all the guys see naked women and think breasts are super nice. but if a girl walks around with a tight top and isn’t wearing a bra and you can see the breasts and nipples, then it’s all of a sudden this huge drama and I just don’t get it, because it just doesn’t make any sense-, um it makes absolutely no sense to me. These types of things, the way they just[…]G1: ⌊It’s so shitty.G3: ⌊If it was up to me I would just run around on the beach topless.[…]G2: For me it’s like, I don’t wear a bra quite often, and I know that people look at me, but I’m honestly really not interested. they can look if they want. I mean that’s basically their problem. so long as they don’t grope or whatever, I really don’t care what they all think.The students reflect together on socially accepted norms for clothing for young women. In doing so, they unanimously problematize the restrictive nature of such norms. The three female students recognize the danger of sexualization and of “groping” when young women do not wear a bra under their clothing, or “run around on the beach topless.” The students oppose these restrictive norms with the notion of free choice, protesting that they will wear what they want. They have each had their own experiences of harassment due to their choice of clothing, and take up the position that they “don’t care” what others think of them, “so long as they don’t grope.” In this sense – unlike in type A – a self-determined sexual identity is declared. The threat of violence that sexualization brings with it is also marked as being a part of their experiences. The girls’ self-representations are guided by their own wishes, in contrast to external expectations (from boys/men). The position of being a victim is linked exclusively to the position of girls, and in delineating their own defense strategies, the female students declare their right to self-determination. Boys are referred to in the passage only as “them,” without further detail.For the male students of this type, resistance to gender norms takes a different form, but it can be compared with the orientations of the female students insofar as it constitutes a break with prevailing notions of gender that legitimize sexual boundary violations. This also becomes apparent through the discussion on the case of the faked account .B5: ⌊Yeah, yeah. (.) Um, for sure um someone from a, from o- our class um made a fake account um on Instagram. and then um sent messages to another guy in our class and pretended to be a woman. and um and sent suggestive messages that she wanted him. um and um the guy from our class, um, he reacted to that. um and I think he then sent um pictures of his penis. (.) the guy who made the fake account, he showed us those images and stuff. and um, so then I told him that I thought what he did was really shitty. because that’s just not OK. and (.) yeah […]. I thought it was totally not OK of him that he pretended to be someone else and (.) um (2) then um lied and stuff. Like, pretended to have feelings for someone else. […]Feminists – they do not explicitly criticize gender relations, their orientation appears to demonstrate critical distance in relation to the reiteration of forms of masculinity in interactions among males, and refuse to be complicit in hegemonic masculinity . This creates occasion for the group Formalists to discuss sexting in a largely critical way:In the following passage, the students of the B1: Yes, it’s because, somehow it’s just assumed to be normalB3: ⌊Yeah.B1: That’s my feeling. I don’t know. or people don’t think it’s so bad or something.B3: Yeah, that’s it, yes, that’s how it is. and it’s your business if you send something like that or if you do something like that. that’s always what (.) people say in the end.B1: Mh.B3: But for me, I have to say I’m glad, um, that I’ve never received something like that or anything else, because I, (.) I’m just fundamentally against sending things like that at all.The students criticize the assumption that image-based intimate communication is normal. In doing so, they suggest that the consensual exchange of images should also be refused. They are guided discursively by restrictive moralizing norms, and reject an orientation toward consensual processes of negotiation and individual possibilities for action.Values-Oriented Girls’ Group also distances itself from the practice of exchanging images. In the following passage, the students discuss a situation in which the sext of a fellow student is made public among their year group. The event is associated, for these students, with “forgetting.”The G1: So I-. so, if we’re talking about images-. So, I can still remember. I don’t know if you know, um. […] but, a girl from our year group sent her boyfriend a shot of her arse and that image also got around.G2: Ah, yeah.G4: Yes.G3: Yes.I: And what happened?G1: So the girl said it wasn’t her in the picture, although you could te-tell that it was her jumper and stuff in the picture. and yeah, then people shit-talk about her. a- like calling her bitch, whore and stuff. And yeah.G4: But also with things like that for example. so I wouldn’t really have thought about it again. things like that also get forgotten pretty quickly I think.G2: Yeah.[…]I: Can you say a bit more about it, what it was like and what happened afterward?exactly really. but, the girl sent it to her boyfriend. and when they broke up, the image like, (.) the image got around. and then some people got in trouble for sending it and stuff, actually in my friend group. um, and (.) yeah, then there was a fight. then there were arguments too, yeah, you’re not my friend anymore, that’s, (2)G1: So I don’t know G4: ⌊MhmG1: ⌊yes so, but (.) we were younger back then. that all happened one, maybe 2 years ago. and yeah, (.) now i- it’s more or less forgotten. now people don’t really think ab- about it and it’s forgotten. (2) Yeah.Values-Oriented Girls’ Group positions itself as being detached from the events. They are only affected in the sense that there were arguments that arose within their own friend group; the girl who is the victim of the incident is not mentioned at all. Thus it is not the event itself, but rather its victim that has been forgotten. Questions relating to the responsibility of the person who shared the image, or about caring for a fellow female student whose rights were violated, are not important to the Values-Oriented Girls’ Group – unlike for type B. The only relevant frame of reference is one’s own circle of friends, which in this case was threatened with division due to an argument over possible participation in sharing the image. By using “forgetting” as a model for defense and repression, the Values-Oriented Girls’ Group positions itself as being beyond the practice of sexting and denies its own involvement in this practice.The group reports that an intimate image was forwarded – although the formulation “the picture got around” side-steps naming an involved human actor in this process. The female student depicted in the image was apparently “shit-talked” as a “bitch, whore.” The denigration of the girl, including using an insult that implies she might be selling sex, is not contradicted by the participants in the group discussion. Instead, the shared act of remembering is coupled with the statement that “things like that … get forgotten pretty quickly.” That the image was shared among their immediate friends is a fact, yet the Those belonging to the type “The Disapprovers” position themselves as being both outside of, and individually unaffected by, societal discourses in relation to gender. Although this type refers to pre-existing gender differences, and even reflects on them by drawing on examples of gender-specific clothing, they dismiss these differences as being irrelevant on a personal level.G4: Ah yes, um, there’s also a boy, wh-who I also know. and his girlfriend also like online (.) very provocatively-. I mean, they broke up. and she reacted to that very provocatively […] I mean, writing that he’s a son of a bitch, that sort of thing. and that you-, that we should keep away from that wanker and that kind of thing. and, um, so that was then-. that was what happened. and this girl for example, she’s pretty explicit. now she shares around that she has a new boyfriend, and she shares things about herself around a lot too. I mean, like she’s in a white shirt, only has a bra and undies on, and her boyfriend’s lifting her up, that kind of thing. I mean, she also uploads stuff like that.Values-Oriented Girls’ Group details the behavior of another girl who sends out “very provocative” digital warnings after the end of her relationship. The students criticize that the girl publishes intimate images of herself with her new boyfriend. Similar to type A, here, a denigration of feminine erotic self-representations takes place in the mode of slut-shaming, without further connecting this to gender roles. Males are mentioned in this narrative only as ‘appendages’ of the apparently ‘morally corrupt’ actions of the girl. The representation focuses exclusively on the individual orientations of the girl; the narrative voice remains consistently distanced. A personal criticism is not formulated, nor are alternative forms of sexual communication mentioned. Rather, the behavior is detailed from a distanced position.This female student from the Formalists, too, entirely exclude gender relations from their narrative, and do not reflect on the framework of social conditions. Rather, the orientation they bring to the conversation is an individualization that eradicates gendered difference. For example, they state that it does not make a difference if girls are seen in the changing room in their underwear or in a bikini in the swimming pool. Additionally, they do not recognize a difference between boys and girls in underwear, establishing that there is no need to make “a big drama” out of such aspects. Through this – unlike with type B – prevailing gender relations are not criticized, but are denied completely and in that sense also implicitly fortified.The boys’ group the This research project investigates sexting among adolescents through the interrelation between sexual boundary violations and digital media, aspects that have rarely been illuminated to date. As described above, adolescence is a key phase in the process of sexual socialization – a phase that increasingly unfolds in part via digital media. Digital media, like analog spaces, provide both spheres of possibility for the crystallization of a self-determined sexual habitus, and also pose risks due to the possibility of non-consensual sexual communication. Through such boundary violations, the process of sexual socialization can be negatively influenced, insofar as it restricts self-determined sexual forms of expression and modes of experience  are ‘themselves to blame’: this is the individualizing assumption operating within this orientation. Such a construction of normativity goes hand-in-hand with orientations toward gender stereotypes that allow sexually active boys to fulfill masculine norms with confidence and irony, objectifying masculinity . On the Members of type B, “The Reflexive-Criticals,” are also involved in sexting practices. While for type A, adolescent experimentation and the possibility for mutual recognition are foregrounded, type B additionally reflects on the non-consensual forwarding and publishing of intimate images as a transgressive practice. In this group, such practices are repudiated. The normativity of adolescent affirmation through sexting is acknowledged and – in the spirit of sexual self-determination – both actively claimed and partly tested out, even as it is called into question as a potentially precarious illusion due to boundary violations. The efficiency of digital reality is framed as being inimical to the potential of self-defined spaces of possibility. Type B does not place responsibility for non-consensual sexual content in the hands of the persons depicted, but seeks to identify those responsible, while posing questions about personal possibilities for effecting change. “The Reflexive-Criticals” thereby distance themselves from societal gender stereotypes . Throughde facto ascribed to the depicted person. The experimentation of autonomy of this type in the course of sexual socialization occurs in the form of boundary violations against persons considered not being male.Type C, “The Disapprovers,” distances itself from digital adolescent cultures that engage in sexual forms of communication and performances of gender. Students belonging to this type view sexting in general as a practice carried out by ‘others’, a practice they say they do not come into contact with. In their orientations, they view sexting in a blanket way as a “non-normative” practice. Its potential significance for adolescent culture is refuted, and – unlike for type A and B – there is no identifiable interest in adolescent experimentation through intimate digital communication. The relevance of sexual digital communication for one’s own sexual socialization is rebuffed. This abstinent orientation is applied not only to these student’s own positions, but is extended to others who view sexting as a legitimate part of sexual communication. In line with this wholesale rejection, the orientations of type C do not differentiate between consensual and non-consensual sexting practices. Rather, they consider being confronted with sexual communication as a boundary violation in itself. Orientations toward gender, too, renounce differentiation in favor of a supposed existence of equality. Gender-based asymmetries in the experience of sexting are not reflected upon, meaning that gendered stereotypes and power imbalances are implicitly reproduced. This presumed neutrality indicates that orientations to sexual norms that are understood as different are excluded and marginalized. The distanced position in relation to sexting practices makes it impossible to recognize non-consensual sexting practices, meaning that responsibility for boundary violations is The interviewed adolescents position themselves within the field of tension between spaces of possibility and boundary violations see . Most ofGender-critical positions are present only within type B. Gender stereotypes are explicitly reproduced by type A, and implicitly by type C. This finding is particularly relevant in light of the tendency to slut-shame when dealing with sexually experimental girls, and to victim-blame in order to ascribe responsibility for boundary violations to  victims, as also reported in other studies . These pBeyond this, the general construction of normativity is linked to orientations toward gender. Those types who view sexting exclusively within the horizon of individual orientations toward norms – regardless of whether they support or reject the practice – tend to reinforce gender stereotypes and negate real boundary violations, in particular through the mode of slut-shaming (on the significance of gender roles, see also Limitations of the study exist due to the sex-homogeneous design of the group discussions, which may contribute to reifications of gender. In addition, non-binary youth are not represented in the sample. Further, only few adolescents participated in the study who reported own positive experience with intimate digital communication. A broader study with a larger number of participants with diverse backgrounds could differentiate and validate the findings. Including non-binary youth, as well as broader consideration of diversely oriented youth overall, would help to further differentiate the findings on dichotomous heteronormative understandings of normality. It could also lead to greater sensitivity to the risk of reifying these adolescents through the research process. Furthermore, in order to explicitly reach non-binary and gender-non-conforming youth and fruitfully explore their lifeworld interaction via digital media and sexuality, we would need a more gender-diverse sampling. In addition, lgbq adolescents would have to be explicitly addressed, since a significantly higher level of media-mediated erotic and sexual communication is recognizable in this group .Further studies would need to look for settings in which a positive attitude toward sexting is associated with a gender-critical orientation. Further research is needed with mixed-sexed groups to control for possible gender bias. However, a study based on mixed-gender group discussions of adolescents in Australia generated findings similar to our analysis . In addiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Board of the German Educational Research Association. The participants provided written informed consent to participate in this study.JB initiated the study and did the sections Introduction, Methodologies, and Findings. JB and CW collaborated the data preparation and analysis, did the section Results, and wrote the literature review section. All authors discussed the design, interpreted the results, contributed to drafting and revision of the manuscript, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Home care aides are a rapidly growing, non-standard workforce who face numerous health risks and stressors on the job. While research shows that aides receive limited support from their agency employers, few studies have explored the wider range of support that aides use when navigating work stress and considered the implications of these arrangements. To investigate this question, we conducted 47 in-depth interviews with 29 home care aides in New York City, focused specifically on aides’ use of support after client death. Theories of work stress, the social ecological framework, and feminist theories of care informed our research. Our analysis demonstrates aides’ extensive reliance on personal sources of support and explores the challenges this can create in their lives and work, and, potentially, for their communities. We also document aides’ efforts to cultivate support stemming from their home-based work environments. Home care aides’ work stress thus emerges as both an occupational health and a community health issue. While employers should carry responsibility for preventing and mitigating work stress, moving toward health equity for marginalized careworkers requires investing in policy-level and community-level supports to bolster employer efforts, particularly as the home care industry becomes increasingly fragmented and non-standard. Non-standard work—including temporary, agency-based, contract, and gig work—is increasing , with urHome care aides, including personal care aides, home health aides, and home-based nursing assistants, comprise one of the fastest growing job sectors in the United States due to the increasing older adult population and ongoing deinstitutionalization of care . They prMultiple dynamic factors create precarity, job insecurity, stress, and poor health outcomes for the home care aide workforce. First, the structure of the home care industry is highly fragmented, leading to challenges for organizing and advocacy. At the same time, the industry is largely federally funded through Medicaid, which has kept wages low, with a median wage of $11.52 per hour in 2019 ,7,8. As While home care aide stress has been well-documented, in this article, we explore the forms of support aides use to buffer and navigate work stress. Since the 1990s, support at work has been considered a key lever in workers’ stress and well-being on the job. Notably, support in work stress models centers on support provided by employers, as an acknowledgement that employers should take responsibility for supporting employees to reduce work stress. Karasek first conceptualized the importance of workplace support as a critical addition to his conceptual job strain model , which showed that jobs with high demands result in less job strain in the presence of control and support . HoweverIn order to explore these topics, we examine the experiences of home care aides in New York City with a particular stressor, client death. Client death is a useful focal point because it can lead to both substantial emotional stress from the loss of a close relationship and job insecurity while awaiting a new client, which can also interact with and amplify each other . As a reWe ask these questions with the goal of better understanding how support that prevents or mitigates job stress works in practice. Importantly, we also seek to bring the complexities of support more vividly into view through the models we use to guide our research. Toward this latter goal, our research is informed by three bodies of theory. The first of these are the work stress models, such as the JDCS and the ERI, discussed above. These theories provide the primary frame for our interest in expanding the notion of support to include support beyond the job in order to better reflect the realities of carework and of non-standard low-wage work. Notably, the only non-job-based support that has been included in work stress models thus far is what has been called “personal resources” . HoweverIn this study, we conducted an inductive, theory-informed thematic analysis in keeping with the realist approach described by Ritchie and colleagues . Specifin = 27). After that, we continued recruitment via snowball sampling with a particular focus on purposive sampling of additional Latinx aides (n = 2). In Summer 2020, we also recontacted aides who had participated in October 2019–January 2020 to better understand aides’ experiences with client death, stress, and support during the pandemic (n = 18).We purposively sampled participants who had worked for at least one year as an aide and had experienced at least one client death. Because of resource limitations, we only sampled English-speaking aides though we sought to sample aides who were born in the US and outside of the US, as well as aides who were Black and Latinx See . We condn = 6) identified as Latina. Aides had a wide range of experience in home care, from 1 to 27 years, and were a mix of those with limited or no experience working on hospice cases, and those with more hospice experience. Almost all aides identified as religious (n = 27), and a majority of these identified as Christians.A description of the resulting sample can be found in Initial interviews were in-person and mostly conducted on site at agencies, while Summer 2020 interviews took place via phone or Zoom due to pandemic restrictions on in-person data collection. All interviews were semi-structured with substantial opportunity for participants to suggest the direction of the interview. A guide helped structure these interviews see  and evolTwo authors (ET and ML) led the development and refinement of coding schemes for the two sets of interviews , which were based on close readings of the transcripts and fieldnotes. Transcripts were then coded in Dedoose, and coding schemes continued to evolve through regular consultation among coders. The analysis presented here focuses on the sources and types of support that aides used. The importance of problems that arose in using support emerged during analytic memo-writing and team interpretation of coded data. Finally, we used a range of strategies to increase quality including practices of reflexivity (on the part of participating coders), triangulation , and peer debriefing with experienced authors outside of the coding team  . All stuIn the sections that follow, we describe in detail the sources of support that aides used to navigate client death. We explore job-based support, personal support, and sources that blend the professional and the personal. We examine the key features of each support category and aides’ experiences with these sources of support. We then focus on two of the problems inherent in relying so heavily on personal and blended source of support. Finally, we briefly describe the changes in support we documented during the COVID-19 pandemic. Note that, because aides may develop and refine their support-seeking over time, we show each aide’s years of work experience when presenting data.Of the various types of support available to aides in relation to client death, the literature has focused most closely on the job-based sources of support, as described earlier. Like this literature, our participants emphasized the important role that coordinators, the agency staff who assign and manage cases, can sometimes play in providing both emotional support and instrumental support  that proFar more commonly used by aides in our study were personal sources of support. We define personal sources of support as support that is fully outside of the domain of work—for instance, from the aide’s family, friends, or religious community. When these supports were present, particularly family support, aides’ experiences with client death overall seemed far less negative. In keeping with definitions of personal resources in existing work stress models, we also include ways that individuals supported and relied on themselves in this section.Aides’ family members and friends were sometimes the sole source of support for aides. Aides noted multiple, general ways that these individuals help them cope emotionally. As one aide said, “My immediate family [is a source of support after client death]. I go home and I talk to my husband and be with my kids, you know, it just definitely helps me to deal with it” . Another emphasized the singularity of this source of support: “You know, friends and family are always there for support. But basically, that’s it” . In particular, some aides mentioned other care workers in their families and social networks whose similar experiences gave them a unique lens through which to offer support. As one aide said, “I do have an aunt who’s a nurse and she’s experienced death on many occasions, so that’s very helpful.” .In some situations, support from family and friends seemed sufficient after a client’s death. In other cases, family members could dismiss an aide’s feelings or otherwise not adequately attend to them. One aide suggested the inadequacy of her family’s response: “They only say I’m sorry to hear about your passing of a patient.” . Another shared the experience of family members with a more directly antagonistic view toward the work: “Support with me wasn’t in my family cuz they don’t care. My son [says], all of you need to quit. I said, ‘Well, [are] you gonna pay my rent?’” .Religious practices—prayer, most commonly—and religious communities were also discussed as central forms of support for many aides following client death. While aides spoke frequently about praying on their own, others sometimes looked for company through prayer, as in the case of this aide: “The spiritual approach is that there is always help available. Personally, there’s a prayer hotline that can be used when I can’t go within myself to do it ” . When possible, attending church further underscored the availability of help: “Yeah, it took me about a week [to recover from a client’s death], because I had to go to church. I go to church and it rejuvenates me. If I can talk to the pastor, then that helps me, because they always help me to take myself out of the equation” . Another aide described a difficult experience with a client whose case had included Sunday hours and had thus prevented her from attending church for some time. In going back to church, she said, “That’s where I started to feel much relief” .Aides also spoke often of using individual coping approaches and beliefs to support themselves through client death. In addition to prayer, as discussed above, there were two primary mechanisms of self-support in our data: Keeping busy and making peace with client death. Several aides boisterously listed the many activities in which they involve themselves to help keep busy and move on from a client’s death. One aide said that she can “make [her] own therapy” in this way. “You read a book, you do some game, you watch TV, you put the radio, you know, you dance. […] I always have something to do. And then if I don’t have something to do, I look for, you know, like something—let’s go outside” . Others found ways to address the death more directly and come to terms with it. As one aide said, “I can get myself together very fast, by considering that, you know, this is their time and they have to go” . Another spoke about how suffering at the end of life helped her accept death: “If people are dying, and they’re really, really suffering, just let them go” .Personal sources of support, and especially reliance on oneself, were also the only forms of support discussed when it came to the financial implications of client death. Though aides sometimes described loss of income after these events, they largely accepted it as part of the job. As one aide said, “Did I see a change [after the client’s death]? Let me see. Just losing another client. Losing a client the money went down a little bit, you know what I’m saying?” .The primary tactic that aides described for navigating the financial implications of client death was to actively pursue new cases at the agency if they are not quickly reassigned. If aides were not successful, then they might seek work opportunities at another agency, and many aides in our sample worked at least some of the time at other agencies in order to secure sufficient hours. A few also described engaging in other forms of work as well: “Well, if I needed financial support, I made—I had to find a way to get it. So, with the other skills that I have I would create additional income. I do counseling and nutrition counseling, workshops, lectures, things like that, so I was able to take care of my financial needs.” . For some aides, family members provided a sense of financial security “my husband has a very good job” .Aides might want to take time off after client loss but are typically unable to do so, due to financial pressures. As one said, “I’m not able to take off any time, because of the money that I earn is not enough to have, to even do anything beyond basically basic needs” . When aides do take time off, they often see it as their choice to go unpaid, despite the need for time off stemming from the work. As another described, “No, I didn’t get support from [the agency]. […] I just took my days off. I chose to lose the pay, right. Because I just—because death is real and it’s fragile. Right? And you need a moment to hit the reset” .For most home care aides, the workplace is the client’s home. While some support stems from this work environment, we do not consider this to be “job-based support” for aides, as it is not employer- or union-generated. Rather, we use the term “blended” to acknowledge that while relationships developed in the home with clients, their family and friends, and sometimes with co-workers do emerge from a professional context, they often require active cultivation and interaction outside of the workplace or work hours to function as sources of support. Hence, aides are blending job-based and personal approaches to support. This is a phenomenon that is unique to some types of non-standard work, as coworker and client relationships are more squarely within the context of job-based structures for standard workers.While aides only rarely work on-site together at the same time, aides working for the same agency can sometimes serve as sources of support. Aides described finding support among their peers at in-service classes and sometimes sharing client loss experiences in those settings. They occasionally built friendships that continued outside of trainings, staying in touch through texting, calling, Whatsapp, and, sometimes, in-person gatherings. They described these relationships as limited but usually positive. As one aide who stayed in touch with coworkers by phone and occasionally went out for lunch with them said, “My coworkers actually, you know, they always reach out to each other and give each other support” . We also saw glimmers of the vibrancy of some coworker interactions while we conducted recruitment during in-service trainings: Aides sharing their experiences during class, swapping stories over lunch, saving a seat for someone.However, some aides gestured at the ephemerality and fragility of these relationships in their interviews. As one experienced aide said, “I had this one girl [coworker who she stayed in touch with], but I don’t talk to her no more. Like—cuz I don’t like to talk to people and they turn the conversation on themselves when I’m generally trying to get something out [of the conversation]” . Another aide mentioned developing a group of aide friends during her initial training. The aide said that the group stayed in touch for a while but eventually they grew apart “because we all have our different problems and cases. You don’t have enough time. Families and so on” .Aides sometimes spoke about meaningful experiences of reciprocal emotional support between themselves and family members or friends of the deceased client. Our data indicate that aides might contact a client’s family after client death (or vice versa) and find support through shared grief, revisiting memories, and ongoing social interactions. Sometimes this was through a one-on-one interaction, as in this aide’s experience: “I felt really sad. I don’t know if they [the agency staff] know, but I immediately called the cousin and said I’m so sorry about what happened and that she was a really good person. […] And her cousin was like, “Oh, she spoke so highly about you. She loved you” . Other aides talked about more collective experiences, as in this case: “The family had a little get together and they invited us [the aides], so we were there sharing her spirit and talking about her. So that was a thing in terms of coping, that little get together” .In many cases, this support was short-term as “everybody moves on with their lives” , though we also heard about aides who were in touch with the deceased client’s families in a more ongoing way. For instance, one aide developed a particularly close relationship with her deceased client’s friends. She said, “…you can talk and we laugh about the things that she [the client] used to say. I think if I hadn’t been in touch with them, it would’ve felt sad and lost. […] I would’ve taken it on more” . It had been a difficult case over three years, with multiple hospital stays interspersed among periods of home care. Throughout this time, this aide had worked together with the client’s neighbors and ex-husband to provide care and stay in touch. After the client’s death, they checked in on each other, spent time together, and cooked for one another. Our interview was nine months after the client passed away and this aide reported that she was still in touch with members of this group at least weekly.Note that, in general, aides who had been in the profession longer (and who had thus managed to stay in the profession) appeared to draw from a wider variety of techniques for supporting themselves  and moving on from client death. Other sources of support and the ways these were navigated did not vary markedly by aides’ amount of time in the profession.In addition to demonstrating the importance of personal sources of support and, to a lesser degree, blended sources of support, our data suggest two core problems that might arise in a system that relies on these so centrally: (1) The potential for these venues of support to increase job stress and threaten job security for aides, and (2) the absence of personal and blended support that some aides face.Because these forms of support require operating in a space between professional and personal, they had the potential to create problems with aides’ employers who want the lines between professional and personal to remain clear and impermeable. Our data provide one overt case in which using these forms of support threatened an aide’s job security, but we heard many subtler examples of the stress aides carry in navigating this personal–professional divide. One aide, for example, grieved a client deeply and despite support from friends and family, the persistent pain from this loss led the aide to post a picture of herself with the client on Facebook, without a name and with the words “Rest in peace”. As the aide said though, “I was just so distraught and I was looking for relief, and I’m not going to lie to you, for some reason, that gave me relief” . This was a highly contested practice within the agency context, however. The aide described how another aide reported this post, and as a result, she was suspended from work for three days while the agency investigated the situation. She feared being fired, though ultimately, she was not.I don’t know if they [the agency staff] know, but I immediately called the cousin…” . Her attentiveness to whether agency staff were aware that she had broken the rule about contacting family members after the case ended suggests that this might have weighed on her mind. However, if this aide had tried to follow agency prohibitions, she would not have been able to use this meaningful source of support. The same goes for the others in our sample who used relationships with clients’ families and friends to navigate and soften the experience of client death.While this situation was likely problematic from an agency perspective due to concerns about patient privacy, agency staff strongly discourage relationships between aides and clients and their social circles generally, as has been documented in the literature ,13,15. ASome aides might not have extensive personal support networks, might not feel comfortable using them, or might lack the ability, willingness, wherewithal, or comfort (given agency warnings against these relationships) to cultivate blended sources of support. In the absence of job-based support, and lacking these additional forms of support, aides might be left to navigate a difficult experience alone. One aide illustrated vividly how useful support outside her personal network would be in these situations: “It would have been a good help, you know, if there was some kind of—somebody I could—that’s not my family or, somebody I could bounce things back off again, that’s more support. Somebody outside of my scope of friends and family. Somebody neutral that, you know, that doesn’t know me, that I could just open up to it. And speak about it, and cry or grieve, or if I had to.” We summarize our findings on the sources of support discussed above in During our Summer 2020 follow-up interviews with aides, we asked about the forms of support aides were using while working during the pandemic. Their responses indicate that non-job-based forms of support remained prominent, religion in particular, and that new, though limited job-based forms of support had arisen in the form of phone-based meetings with other aides organized by their agencies and unions. Support from coworkers and clients and/or their families were barely mentioned in these interviews, suggesting that these forms of support might be particularly likely to vanish under pressure given the effort they take to cultivate. Finally, we note that emotional and financial support from friends and family remained central but appeared more strained in these data. One quote from an aide who lost a client to COVID-19 expresses this vividly: “Everybody is busy, everybody is thinking about the pandemic, everybody has different problems and I don’t want to call any friends and say, ‘Oh, my patient died.’ You know, nobody listen. Yeah, nobody say, ‘Oh, I’m sorry.’”  We also heard from aides whose spouses, often the primary breadwinners, were out of work.Reliance on one’s own strength and coping, and on a relationship with God, were strong narratives in these data. As one aide said, “I don’t need much, because I’m pretty self-motivating and pretty much I don’t get saddened too much. I have my own system that I use to help keep me up […] but I think something like that, a better relationship with your [coordinator] or the nurse, would be helpful” . For many aides, faith in God’s ability to protect them offered some relief from the anxiety of continuing work during the pandemic. Conveying the sentiments of many aides who we interviewed, one aide said, “For me, my anchor really is in Christ, the Father, Son, and Holy Spirit. They really help me, because if I get too out of balance or I keep something on my mind too long they just calm my concerns” .A new job-based source of support mentioned by many aides was agency- and/or union-sponsored phone-based support groups and town hall meetings. These sometimes involved hundreds of aides. Some aides noted that these were impractical while they were with clients, too lengthy, not a venue in which they were comfortable, or simply not of interest. Others were more enthusiastic, feeling that the calls offered recognition of the importance of their work, addressed their problems to varying degrees, and offered a chance to vent and a place to better understand the difficulties that other aides were facing. One particularly optimistic aide saw these calls as signs of possible increased support from agencies going forward, saying, “This is the first period—since COVID—that we’ve had a chance to really be in touch with anybody [at the agency] on a personal level, to let them know what we’re feeling, what we’re going through. So, a lot of these things I think they’re working on fixing so that, you know, we could have more support. […] With the support calls, they’re taking ideas or suggestions that we give on those calls, to see how far they can go with it and to create some kind of change in the organization” . Other aides appeared less optimistic about the durability of these new forms of support.Focusing on a single work-based stressor that has both emotional and financial dimensions , we have shown that home care aides draw heavily on their own internal resources and personal social networks in order to address work stress. They also cultivate and maintain sources of support that cross professional-personal boundaries, which can be valuable but require effort and can create job insecurity and their own forms of stress. In contrast, aides in this study derived only minimal and sporadic support from their employer agencies. How aides navigate in the absence of reliable employer support is especially important to attend to as the home care industry in the U.S. moves towards models in which traditional employer agencies are completely absent or less present. For instance, recently, there has been significant growth in consumer-directed models of home care, in which family members or friends serve as paid home care aides, as well as growth in technology-enabled home care services that facilitate the direct hiring of aides by clients and their families .Our research thus highlights the importance of understanding workers and work stress in the context of their lives and communities, not simply their jobs, and provokes us to consider more deeply how work stress models can be integrated with the social ecological framework for non-standard workers . SpecifiOur findings indicate that home care aides receive limited emotional and financial job-based support, and that more supportive infrastructure on the job is essential to reducing their work stress. However, our research also suggests the urgency of recognizing that some support addressing work stress will continue to come from outside of work, due to the general movement toward less standard work arrangements. Thus, we propose that existing models of work stress should better incorporate the wide range of supports workers actually draw on in managing work stress, supports that could be bolstered. In In this model, we conceptualize the key demands and rewards of home care labor as being physical, emotional, and financial. Similar factors were recently described in a qualitative meta-synthesis of home health aides’ occupational health experiences that included studies from the United States, Canada, Norway, Denmark, Ireland, and Sweden .The emotAnother contribution of this model is to highlight the complexities and contributions of blended sources of support for home care aides, not currently recognized in work stress models. Employers have historically done little to actively bolster coworker support, and existing supervisory relationships often increase aides’ work stress ,13. In tDrawing on the social ecological framework, we also note issues at the population level that might arise from the heavy emphasis on personal support, rather than job-based support, to buffer job stress (see “community effects” in Our findings suggest a need for both worker-focused and community-focused policy action, as well as steps that employers should take. Worker-focused policies that could help to address aides’ lack of support and resultant health inequities include paid leave, living wage, affordable health care, and strong worker health and safety policies . NotablyFor employers, our findings indicate that more and better job-based support is needed. Importantly, such support has been shown to be effective when present , and it Finally, this study has some limitations. Most significantly, our sample is not representative of the diverse experiences of aides throughout the U.S. First, we included only aides who marshalled enough support to be able to continue in the job after one or more client deaths. We are thus not able to speak to the support-related experiences of aides who left the workforce, or to assess differences in support. Second, aides were primarily recruited from two agencies in New York City at which aides are unionized and thus likely had more employer/union support than aides have generally. Finally, aides were English-speaking and thus our data do not represent the perspectives of aides who are non-English-speaking and who might have different support systems outside of work. In considering transferability, while our findings are centered on home care aides, we expect that some of these ideas might have relevance for the larger population of low-wage careworkers who also typically lack job-based support. Finally, given our model’s focus on the emotional health and well-being of people whose jobs routinely put them in contact with grief and loss, our research might also be relevant to a range of carework professions, particularly in the wake of the COVID-19 pandemic.Home care workers are a rapidly growing workforce whose importance has only increased as a result of the COVID-19 pandemic. In order to build and support this workforce in ways that allow our society as a whole to thrive, we need to better understand the kinds of support that workers draw on in order to provide this critical form of labor, as well as the effects of working with current patterns of support. Our analysis indicates aides’ extensive reliance on personal sources of support, and on some blended personal-professional sources of support, which can create challenges in their lives and work, and, potentially, for their communities. Job-based support currently plays a limited and unreliable role in helping aides to navigate stress from client death. Home care aides’ work stress thus emerges as both an occupational health and a community health issue. While employers should carry responsibility for preventing and mitigating work stress, moving toward health equity for marginalized careworkers requires investing in policy-level and community-level supports to bolster employer efforts, particularly as the home care industry becomes increasingly fragmented and non-standard.


Background and purpose — Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research.Methods and results — We present the common tasks, considerations, and pitfalls  that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing.Interpretation — We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting. Key concepts presented in this reviewIntroduction to artificial intelligence (AI) and machine learning (ML) and how these relate to traditional clinical research statisticsCommon pitfalls in AI researchHow to measure and interpret AI and ML performance and how to interpret these measuresEthical considerations related to AI and ML in medicineIntroduction of a Clinical Artificial Intelligence Research (CAIR) checklist, which helps to facilitate understanding, reporting, and interpreting of AI research in medicine.Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have become increasingly common in orthopedics and other medical fields. Artificial intelligence, defined in 1955, is “the science and engineering of making intelligent machines,” where intelligence is “the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world” Manning .Machine learning implies models and algorithms that learn from data rather than following explicit rules. Deep learning (DL) is a form of ML that uses large and multilayered artificial neural networks. Neural networks are computational algorithms influenced by biological networks for information processing. They consist of several layers of “neurons” that communicate. By training the neurons how to communicate, interactions develop that solve a particular problem. DL is currently the most successful and general ML approach , software, and new algorithms have paved the way for a revolution in applications and utility. Together these have resulted in new and exciting developments. Examples range from new drug discoveries Fleming  to skin At its core, AI involves automating complex algorithms, which often depend heavily on statistics. Computation allows for calculations and modeling on a scale that humans could theoretically perform but which are too large and complicated to be feasible.AI has its own language and different names for concepts familiar to medical professionals. Using different names obfuscates the fact that these are familiar concepts.In ML, a model is akin to a test. For example, an ML model could test whether a radiograph fulfills the conditions for containing a fracture. Depending on how well the image meets these conditions, it will calculate a probability for a fracture in the image. In contrast, regular statistics investigate individual features’ contribution to a particular outcome, e.g., how much does smoking or alcohol contribute to the risk of having a fracture. The core difference is that AI models generally have a much richer set of features, often in the thousands. Individual features merge into patterns and regularly lose their interpretability. AI models can mostly be considered “black boxes” as the path from input to output is often unclear. The main objective is, therefore, usually predicting a specific outcome.Another difference from traditional statistics is that many ML models guess the correct answer and improve by studying the errors they make. For example, suppose we present the model with an image. In that case, the model could guess that the probability is 80% that there is a fracture. If we agree that there is a fracture, we can calculate that the error is 20%. By investigating what parameters were not suggesting fracture, we can nudge those parameters into the fracture category to be more likely to predict a fracture the next time. When reporting on AI interventions, the clinical setting is crucial for understanding performance. Clinicians must understand the tasks and performance measures and whether the outcomes are relevant to the clinical setting.A classification task is a task that categorizes observations to a set of known outcomes/classes, for example, type of fracture, normal vs. pathological ECG, or staging of a malignancy. Typically, the model produces a probability score per outcome. When there are 2 outcomes, e.g., fracture or not, we have a dichotomous outcome, a binary classification problem . If there are several possible outcomes, e.g., hip fracture Garden 1–4, it is a multi-class classification task.The model’s core function is to separate the groups, i.e., it is preferred that the network provides probabilities close to 0% and 100% instead of around 50%. For example, an algorithm might state that there is a 20% probability of a fracture. For some purposes, this could be considered sufficient to decide on the absence of pathology, e.g., a suspected type A ankle fracture. In contrast, a scaphoid fracture may be unfortunate to miss, due to the risk of non-union when left untreated. Even at a 20% likelihood of a fracture, we might proceed with an MRI. Therefore, a reliable classifier’s key feature is better separation between groups with few cases in the conflicted region’ in this example, between 20% and 80%.Image analysis  has gathered much attention and success. It entails analyzing and classifying the contents of images, for example, fractures  deals with language and text management. It could entail translating between languages, interpreting a written journal, generating a journal, or describing an image’s contents.Clustering is a form of ML where the AI groups data into classes without prior knowledge. For example, given a collection of radiographs, it is given the task of sorting them into groups. For instance, we could let the algorithm find which fractures are similar instead of manually choosing the groups.Medical data is often skewed, with some outcomes being much more common than others. In general, we are more likely to find a healthy individual than an unhealthy individual. Hence, a negative test for a disease is the most likely outcome. In situations where there are multiple possible outcome types, e.g., an elaborate classification scheme, each outcome becomes less likely. That is, if we have 30 fractures and 3 groups, each subgroup will, on average, contain 10 fractures. In most cases, it becomes even more skewed, as some subgroups are more common than others.By emphasizing rare cases, giving them more weight in computations during training, we can alleviate the imbalances during training.Training is the process of an AI model iterating through a database of cases, with annotations, thereby learning from many examples. One iteration through the entire training set is called an “epoch,” a time-consuming process that can take hours to weeks to reach optimal performance. During this process, it learns the important features of the data.The testing phase is when the model examines examples it has not seen before and on the basis of which it has to provide an outcome. Predictions of the model are compared with the ground truth in order to evaluate performance.During testing, model performance may not necessarily reflect accurate performance, as rare cases will be underrepresented. Unfortunately, recruiting more of the underrepresented classes is labor-intensive. For example, suppose we have a class that occurs once in 300 cases. In that case, we need to review at least 600 images to find 2 examples. These rare cases are usually clinically interesting, and the effort needs to be balanced against the clinical importance.A common practice is to manipulate images at random, e.g., rotating images, to force the network to find features independent of the manipulations applied; this is a form of data augmentation.Some outcomes are so rare that they will not be present in the data, preventing the algorithm from detecting them. It is a fundamental difference from humans, who can learn what a class looks like before seeing it, e.g., a Pipkin fracture, and recognize when first seeing it.An ML model learns by looking at examples. If it learns the training examples too well, it learns the individual patients instead of the problem’s general features, i.e., it learns to recognizes the individual training cases and the expected outcome, rather than the common traits that make up the outcome. Due to the size and flexibility of ML models used, this is a common problem and the reason why the gold standard is to split the data into at least a training and a test set. The test set is kept separate for final evaluation and is not used for training the model and is thus a more objective measure of performance. The same training case, or patient, must not be included in both the training and test set, as this would overestimate the accuracy. A validation set is similar to the test set, but is used to optimize settings for the model during training—and is not always reported. Confusingly, the test set is sometimes called the validation set.When reporting on ML algorithms, the clinical setting is essential for understanding the actual performance—and clinicians and readers must understand the results. There are many methods to measure performance, all with their strengths and weaknesses. In Many of the performance measures presented here are familiar to clinicians from diagnostic testing, e.g., accuracy, specificity, and sensitivity. ML contains a large number of additional performance measures that researchers can report. There is a need to strike a balance between measures that clinicians are familiar with and achieving methodological perfection.When assessing an experiment’s outcome or a diagnostic test, it is common practice to present outcomes in a confusion table. A binary test has 2 possible outcomes. For a binary diagnostic test , we have 4 possible outcomes. Suppose we are using a model to classify an ankle fracture into 1 of 3 outcomes—type A, type B, or type C malleolar fracture, excluding the “no fracture” outcome. We present the resulting 3-by-3 confusion table in There is an underlying decision-making process when an outcome is positive or negative. For example, we might decide that if there is a > 50% chance of the presence of a certain condition, the test is considered positive, and we would get one confusion table. However, if we decided that we need > 90% certainty to decide that a test is positive, we would get less positive test results and fewer false positives (FP). The resulting confusion table would look very different. A screening test might consider a > 20% likelihood as a positive outcome, resulting in many FP but very few false negatives (FN). The threshold where we decide that a test is negative or positive is called the decision threshold or classification threshold.Accuracy is defined as the correct classification rate, i.e., the rate of correct findings. For instance, if we had a data set containing 5 fractures in 100 radiographs and all those fractures were detected, we would have 100% accuracy. Suppose we have a test that always indicates “no fracture,” then the performance of the test would be 95% accurate. The second test is very accurate but has no clinical value. Accuracy has limited value for imbalanced data sets.Sensitivity  and specificity are properties well known to clinicians. Sensitivity measures how likely a test is to exclude or detect a condition correctly. We can always achieve 100% sensitivity by saying that everyone has the condition. We would spot every case with the condition, but get also get many false positives.Specificity represents the true negative (TN) rate, which should usually be high in medical tests, and is balanced with the sensitivity. In a more complex task where we want to differentiate among multiple outcomes, the number of true negatives will dominate for most outcomes, and specificity should generally be high. As with accuracy and other performance measures that consider the TN rate, specificity contains little information of value in unbalanced datasets.Specificity and sensitivity represent the proportion of TP and TN, respectively, and not the probability of a condition.A typical use case for high sensitivity is fracture detection. The Ottawa Knee Rule has a sensitivity of 98%, and a negative test will allow us to not go further with further imaging studies. Specificity is roughly 50%, and half will have false-positive tests. Conversely, we care more about specificity in meniscal tears as these are usually less acute, and the bottleneck is often the availability of MRI. Apley’s maneuver has a sensitivity of 20%. However, a specificity of 90% suggests that requesting an MRI will result in few unnecessary exams once encountered. Ideally, a test or algorithm should provide high sensitivity and specificity. However, depending on the clinical setting, we can choose to sacrifice one for the other.Youden’s J combines specificity and sensitivity into one metric and is a way to summarize them into a single value, ranging from 0 to 1.The FPR is the proportion of negative outcomes that have been incorrectly predicted as positive and should be considered the opposite of sensitivity.Given a prediction, we want to know how likely it is for that prediction to be correct. PPV  answers the question: if we have a set of positive outcomes (cases predicted as positive), what proportion of those outcomes were truly positive? NPV measures the same for negative cases, i.e., if we have a set of negative outcomes, what proportion of those outcomes are genuinely negative?PPV and NPV, in contrast to specificity and sensitivity, give the probability of an outcome based on the prevalence in the sample.Precision and recall, as terms, are commonly used in ML studies but relatively unknown in medicine. In epidemiology, precision is the PPV, while recall is the sensitivity. Neither precision nor sensitivity takes into account TNs and, as such, they are less affected by class imbalances in data. Class imbalance has become more recognized in medical AI. It has become more common to use performance measures that take class imbalance into account. Precision and sensitivity are less sensitive to class imbalance. The F1 score, or Dice score, is a way to combine precision and sensitivity, and can be understood in terms of data overlap, as in Other good performance measures exist but are not commonly encountered, e.g., the Matthews correlation coefficient (MCC) and other F-scores. See the supplement for details.We derived the previous performance measures from the confusion table based on classification outcomes. AI classification systems usually yield a probability score as output  and classify data according to a decision threshold. These thresholds are generally arbitrary . However, they can also be tuned on a separate development dataset or derived from the literature. We constructed the confusion table based on whether we detected a condition or not, depending on whether it was present. The outcome of the decision-making process depends on the classification threshold.To assess the predictions without relying on a single classification threshold, we can compute the negatives’ rate for all thresholds  and plot them in a curve. It is not feasible to compute the confusion matrix and outputs for all possible thresholds. Instead, we compute the confusion matrix for some thresholds, combine them into a curve, and estimate the area under the curve (AUC).By computing AUC, we can estimate generic model performance. The two curves mostly studied are the receiver-operating characteristic (ROC) and precision-recall (PR) curves. In When research literature mentions AUC, it usually refers to the area under the ROC curve (AUC). We will use AUC for the area under the ROC curve unless otherwise explicitly stated. The ROC curve plots the sensitivity (the y-axis) against the FPR (the x-axis) for all decision thresholds in order to obtain a curve. Computing the area under that curve gives us the AUC, which measures the model’s overall accuracy. The ROC curve’s idea is to measure the model’s ability to separate the groups by penalizing based on how wrong probabilities are.Supplementary material.As AUC depends on the specificity, which includes the TN outcomes, it is sensitive to imbalanced data. For a clinical trial or practical application, high AUC risks overestimating performance, because it is related to the accuracy, which is sensitive to data imbalance. One should consider a different performance measure for imbalanced data sets. However, we usually encounter AUC during research and development, where it is used to measure the overall model performance. It does not confine the model to a specific decision threshold, as it is computed over all thresholds. The AUC is well understood, easy to interpret, and has nice properties. See Precision is the same as PPV, and recall is the same as sensitivity. The PR curve illustrates the tradeoff between precision and sensitivity and measures the model’s ability to separate between the groups. As neither precision nor sensitivity depends on TN, it is considered well suited to class imbalance data.Using AUPR to assess a model’s performance, as with the AUC, will measure the model’s performance in a way that is not affected by the classification threshold  is more intuitive .IoU is used to determine an image segmentation task’s performance in image data, such as radiographs, CT or MRI slices, or pathology slides. It is a measure of the pixel overlap comparing the area of overlap with the combined area of the predicted and actual location (or ground truth) in percentages. While relying on the application and source, it is common to consider > 50% of pixels a sufficient overlap for success.Supplementary material).A localization task can also be used to determine the presence or absence of pathology. As this is a type of classification, the same measures used for other classification tasks are suited, e.g., ROC analysis (Chakraborty Another option is the region of interest (ROI) analysis, where the image is divided into regions. For example, parts of a brain scan could be divided into their respective cortexes. For each region, the rater assigns a probability that a lesion is located in that region. Plotting the ROC curve, with the number of regions falsely assigned as having a lesion, the performance can then be studied using ordinary ROC analysis. In ordinary ROC analysis, the patient or image is the unit to be observed. In contrast, each region is of interest in ROI  is a common performance metric for continuous data. It computes the average squared error between the predicted and actual value. Squaring the error penalizes large errors, and it is thus more sensitive to outliers. Usually, the square root is taken from the MSE, giving the RMSE, which benefits from having the same unit and is easily relatable to the original value.MAE, or mean absolute deviation, finds the average distance between the predicted and actual value. MAE is less affected by outliers than MSE, as it does not square the difference in values.Getting an AI model to detect the presence of pathology (2 outcomes) to high accuracy is generally easy, and a trivial task with limited utility. For example, most orthopedic surgeons or radiologists are good at quickly spotting fractures or other pathologies. Rather, use-cases where an AI model will be useful are to classify, locate, or detect many different outcomes or make difficult classifications.A model will perform differently for each outcome, and we have to take this into account. As the number of outcomes increases, we will have to summarize multiple performance measures for all outcomes. As we would do with a group of individuals where we report a mean, we need to merge multiple outcomes into meaningful summary statistics.Taking averages of the individual groups would give excessive importance to small groups. In AUC from  would beFWA = (24 · 0.8 + 137 · 0.93 + 47 · 0.86)/(24 + 137 + 47) = 0.90.AUCFWA = (24 · 0.27 + 137 · 0.87 + 47 · 0.63)/(24 + 137 + 47) = 0.75.FWA can be applied to any metric, for instance, AUPRMedical language generation involves the generation of medical text , with or without the use of input . For example, Gale et al. (2018) trained a system to produce descriptive sentences to clarify deep learning classifiers’ decisions when detecting hip fractures from frontal pelvic radiographs.The most common word-overlap measures in medical text generation are BLEU  is in biomedical captioning the most common ROUGE variant. It measures the ratio of the length of the longest word subsequence in the human-generated text shared and the system-generated text. The measure complements BLEU by focusing on the human-generated text’s length instead of the system-generated text.We also note that various language generation evaluation measures exist, such as METEOR  or mean absolute error (MAE). Both translate to values interpretable on the original scale and are familiar to many clinicians from traditional statistics.Historically, we have been more interested in RMSE as outliers tend to be a major concern. For example, after wrist fracture surgery, most patients will have low VAS-pain levels. We are then primarily interested in identifying failures that risk high levels of VAS. Machine learning allows for new applications and, under some circumstances, we will at times prefer the MAE. For example, if the system draws a bounding box around a fracture, the box must be close to the fracture site most of the time. If the objective is to enhance efficiency while quickly viewing images, we are less concerned with rare, complex fracture cases. These will, regardless of the bounding box, require more attention.The F1 score is a common performance measure for segmentation performance in images. However, we argue that its interpretation is non-intuitive compared with the IoU, as shown in If we compare to a known text, such as in biomedical image captioning, we can use BLEU and ROUGE-L  ChecklistSee AI and ML will most likely impact medicine in more ways than we can imagine. Arguing that clinicians need to be involved, we began by describing different tasks and pitfalls in machine learning and shared some ways to address them.We followed by presenting the related concept of performance measures. Performance measures describe the result of the study. Using the right performance measure will give a correct context to the outcome. However, performance measure choice is not always clear and occasionally depends on an experiment’s stage and the audience. For a prospective study or development of an AI model, a measure such as AUC is appropriate. When we use an AI model as an intervention in a clinical trial or in a production setting, where we implement a specific AI system, the actual expected performance is more important. MCC or precision-recall analysis with AUPR and F1-score are more suitable, as AUC could overestimate the model’s performance  and JO by grants provided by the Karolinska Institute. MG is a co-founder and shareholder in DeepMed AB. CL is an employee and shareholder of Sectra AB.Click here for additional data file.