Do social protection programmes have a causal effect on suicide mortality? A protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Recent international and national strategies to reduce suicide mortality have suggested that social protection programmes may be an effective multisectoral response given the link between material deprivation and suicides in observational studies. However, there is a lack of evidence on the causal relationship between social protection programmes and suicide, which may hinder substantial national budget reallocations necessary to implement these policies. Social protection programmes are government interventions that ensure adequate income now and in the future, through changes to earned income (eg, minimum wage increase) or social security (via cash transfers or cash equivalents). Our review aims to evaluate the existing evidence on a causal relationship between social protection programmes and suicide mortality by examining all relevant experimental and quasi-experimental studies between January 1980 and November 2021. METHODS AND ANALYSIS: The review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. We will search references published between 1 January 1980 and 30 November 2021 in 10 electronic databases, including MEDLINE (PubMed), PsycINFO, EMBASE and Applied Social Sciences Index Abstracts. Seven reviewers will independently participate in screening studies from titles, abstracts and full texts across all the stages. Experimental (ie, randomised controlled trials) and quasi-experimental studies (ie, non-randomised interventional studies) written in English, French, Spanish, German, Chinese, Korean and Japanese examining the impact of income security programmes on suicide mortality were included. Meta-analyses will be conducted if there are at least three studies with similar income security programmes. ETHICS AND DISSEMINATION: Our proposed review does not require ethical approval. In collaboration with our community partners, we will develop a policy brief for stakeholders to support efforts to implement social protection programmes to help prevent suicides. Our findings will be presented at conferences, published in a peer-reviewer journal and promoted on social media platforms. PROSPERO REGISTRATION NUMBER: CRD42021252235.
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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 | 0.129 | 0.184 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.035 | 0.041 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.053 | 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".