A Self-Harm Awareness Training Module for School Staff: Co-Design and User Testing Study
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of self-harm among adolescents is a significant public health concern. School staff are often the first professionals to notice when a young person is self-harming and are in a unique position to intervene and offer support. However, research indicates that many school staff members feel ill-equipped and lack confidence in how to respond. Negative or dismissive responses may discourage young people from seeking further help. There is an urgent need for targeted training interventions to equip school staff with the skills and knowledge necessary to support students who self-harm. OBJECTIVE: This study aimed to co-design a self-harm awareness e-learning module for school staff in the United Kingdom. METHODS: The e-learning module design and development was guided by a person-based approach over three participatory design cycles: (1) co-design sessions with experts in mental health, self-harm, and school-based training; (2) workshops with school staff to co-design the e-learning module components and explore their views on supporting students who self-harm; and (3) user testing of the prototype and focus groups with school staff to explore acceptability and feasibility. Data were thematically analyzed using the framework method. RESULTS: Training content, videos, and quizzes were developed in collaboration with a panel of experts. Co-design workshops with school staff (n=11) informed the prototype module design, structure, and scripts for the training content and filmed scenarios, as well as highlighting potential barriers to and facilitators of implementation. User testing of the prototype with staff (n=20) yielded high usability ratings, demonstrating high levels of acceptability. Analysis of the qualitative user testing data generated four themes: (1) usability, (2) content and design, (3) feasibility, and (4) views on how the training improved knowledge and confidence. CONCLUSIONS: The Supportive Response to Self-Harm e-learning module was developed to enhance school staff's knowledge and confidence in responding to self-harm. It was created with a user-centered design and a person-based approach and underpinned by psychological theory. Initial findings indicate that the training is acceptable and feasible. Further research will involve a mixed methods pilot feasibility study to assess the effectiveness of the program. This will provide the necessary evidence for a large-scale rollout in schools.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".