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Record W4410248534 · doi:10.2196/69309

A Self-Harm Awareness Training Module for School Staff: Co-Design and User Testing Study

2025· article· en· W4410248534 on OpenAlexvenueno aff
Anne‐Marie Burn, Hayley Gains, Joanna Anderson

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsPreprintCoproductionHarmPsychologyComputer scienceMedical educationApplied psychologyMedicineWorld Wide WebSocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.283
GPT teacher head0.561
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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