National suicide management guidelines with family as an interv’ention and suicide mortality rates: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Suicidal behaviour remains a major public health challenge worldwide. Several countries have developed national suicide guidelines aimed at raising awareness of and preventing deaths by suicide. One of the interventions often mentioned in these national guidelines is the involvement of family members as a protective factor in suicide prevention. However, the level or type of family involvement required to reduce suicidal behaviour is not well understood. Thus, in this systematic review, we seek to determine the effectiveness of family-based interventions as a suicide prevention tool, by comparing suicide mortality rates between countries whose national suicide prevention guidelines include family-based interventions and those whose do not. METHODS AND ANALYSIS: MEDLINE, EMBASE, PsycINFO, Web of Science and WHO MiNDbank databases as well as grey literature such as National Guideline Clearinghouse will be searched. National guidelines for suicide prevention published within the last 20 years (between 1999 and 2019) will be included. Results will be analysed using thematic and qualitative analyses. ETHICS AND DISSEMINATION: The findings of the study will help improve the efficacy of national suicide prevention strategies. Findings will be disseminated using easily accessible summary reports and resources to primary end users. PROSPERO REGISTRATION NUMBER: This protocol has been registered on PROSPERO (CRD42019130195).
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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 | 0.051 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.007 |
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".