Developing recommendations to improve identification, prevention, and response to suicide thoughts and behaviours among post-secondary students: A mixed methods study
Bibliographic record
Abstract
Objective: This study aimed to generate recommendations regarding how to identify, prevent and respond to suicide thoughts and behaviors among post-secondary students. Methods: A convergent mixed-methods design with Nominal Groups Technique (NGT) was used. Post-secondary and high-school students and their caregivers generated and ranked recommendations. A Codebook Thematic Analysis approach guided analysis of the NGT-discussions and extended understanding of recommendations. Results: 88 individuals participated in 21 panels. Five key recommendations were identified: (1) increase student and staff education regarding suicide identification, prevention, and awareness of existing supports; (2) enhance rapid access to supports for those experiencing a crisis; (3) improve institutional academic supports for students following crisis; (4) reduce stigma; (5) improve communication regarding on-campus suicide. Common themes included perceived impact of attitudes, institutional barriers, and peer-support on suicide thoughts and behaviors. Conclusions: These recommendations can inform the development of student-centred interventions for improving mental health supports.
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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 | 0.073 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".