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Record W2795586387 · doi:10.1177/0020731418767548

The Impacts of Social Protection Policies and Programs on Suicide: A Literature Review

2018· review· en· W2795586387 on OpenAlexaff
Chungah Kim

Bibliographic record

VenueInternational Journal of Health Services · 2018
Typereview
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAssociation (psychology)Diversity (politics)PsychologySuicide preventionInclusion (mineral)Human factors and ergonomicsEmpirical researchPoison controlMedicineEnvironmental healthSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Despite vigorous interest in showing the association between economic determinants and suicide, not many studies have focused on the social protection that can moderate the detrimental impact of the economic environment on suicide. This article is the first to review the relationship between suicide and social protection. In this article, I summarize the empirical findings and theoretical approaches in published papers on the relationship between suicide and social protection, and I identify knowledge gaps for future studies. The review included all quantitative and qualitative articles published in peer-reviewed journals, regardless of study setting, language, and time period. Among 19 papers meeting the inclusion criteria, 16 studies reported at least one negative association, 2 studies failed to prove a statistical association, and 1 study showed ambiguous results. However, due to the heterogeneity of contexts, the diversity of indicators of social protection, and the paucity of theoretical mechanisms for interpreting the results, further research is required in this area.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.106
GPT teacher head0.521
Teacher spread0.415 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes1
Has abstractyes

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