Co-Developing Content Updates for the Card Sort Task for Self-Harm–Digital (CaTS-D) With People With Lived Self-Harm Experiences: Pilot Study and Thematic Analysis
Bibliographic record
Abstract
Background: Self-harm is a significant global concern with multiple negative outcomes. Self-harm research tools typically focus on single risk factors, meaning the temporal interplay between factors and their impact on self-harm is unknown. The Card Sort Task for Self-Harm (CaTS) addressed these deficits by using 117 cards to examine multiple self-harm factors. In-person research is time-consuming, costly, and limits participation opportunities. Developing an electronic version of CaTS (Card Sort Task for Self-harm-digital; CaTS-D) is necessary to address these issues, capture large datasets, and provide a stronger evidence base. Since CaTS' inception, understanding of self-harm has evolved, including increasing awareness that lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other minoritized gender and sexual identities (LGBTQIA+) people are at high risk. Updating CaTS is essential to ensure its relevance to both LGBTQIA+ and cisgender-heterosexual self-harm. Objective: We aimed to present results from two studies. Study 1 is a pilot study assessing the feasibility of CaTS-D. Study 2 used qualitative interviews to identify additions or amendments to CaTS to increase its relevance as understanding of self-harm evolves. Methods: Study 1 recruited UK residents (N=13, aged 18-30 y) with lived self-harm experience. Feasibility and acceptability of CaTS-D were assessed using the Systems Usability Scale (SUS) and visual analog scale (VAS). Study 2 recruited UK residents (N=13; LGBTQIA+ n=9 and cisgender-heterosexual n=4; aged 21-29 y) with lived self-harm experience to one-on-one interviews. Results: Study 1 found CaTS-D to be a feasible web application for use in self-harm research. VAS data showed no significant difference between pre- and poststudy mood (t9=1.59; P=.15). In Study 2, thematic analysis resulted in 13 additional cards (eg, "Before 6 mo;" "I don't feel comfortable in my body;" "I was bullied on social media;" and "Self-harm gave me a feeling of control"). Cards were worded clearly, but minor amendments to wording on cards to increase LGBTQIA+inclusivity were identified (ie, changing "boyfriend/girlfriend" to "partner"). While participants felt selective additions were necessary, too many may overwhelm participants. Therefore, future additions should be carefully considered. Conclusions: Pilot-testing shows CaTS-D is a usable, feasible web application to examine self-harm and capture large datasets. Importantly, completing CaTS-D does not negatively impact participants' mood. Updates and key additions were made to CaTS from consultation with people with lived self-harm experiences. These increase the relevance of CaTS and ensure LGBTQIA+ inclusivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".