Falling between the cracks: The effect of using different levels of suicide risk exclusion criteria on sample characteristics when recruiting for an online intervention for depression
Bibliographic record
Abstract
BACKGROUND: Despite a strong link between suicide risk and depression, a recent literature review found that many effectiveness studies for online depression interventions exclude individuals at risk of suicide. This study scrutinizes how different suicide risk exclusion criteria impact recruitment rates and final sample characteristics. MATERIALS AND METHODS: Two recruitment periods for an online depression intervention trial utilized different suicide risk cutoff exclusion criteria, a one-point difference on the last item of the Personal Health Questionnaire (i.e., more than 0 (Not at all) vs. more than 1 (Several Days)). Bivariate statistics were used to assess differences in recruitment rates and sample characteristics between these two recruitment periods, while all other eligibility criteria and recruitment strategies remained consistent. RESULTS: The recruitment period using the least restrictive suicide risk exclusion criteria yielded twice as many participants; however, recruited sample characteristics did not significantly differ among demographic or clinical characteristics, despite observable trends. DISCUSSION: Researchers should carefully select suicide risk exclusion criteria that balance recruitment rates, study budgets, and sample selection biases, while minimizing participant harm. Moreover, researchers are urged to report suicide risk exclusion rates and consider these exclusions when interpreting results. Limitations of the results are also discussed.
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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 | 0.282 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".