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Record W4360816549 · doi:10.1027/0227-5910/a000902

A Public Health, Whole-of-Government Approach to National Suicide Prevention Strategies

2023· editorial· en· W4360816549 on OpenAlex
Jane Pirkis, David Gunnell, Keith Hawton, Sarah Hetrick, Thomas Niederkrotenthaler, Mark Sinyor, Paul S. F. Yip, Shane Robinson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrisis · 2023
Typeeditorial
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsGovernment (linguistics)Public healthSuicide preventionPolitical sciencePublic administrationPublic relationsPoison controlEnvironmental healthMedicineNursing

Abstract

fetched live from OpenAlex

Many countries have national suicide prevention strategies, all of which aim to reduce suicide and many of which also address self-harm more generally (World Health Organization, 2018).In this editorial, we argue that national strategies could be strengthened through an increased focus on the social determinants associated with suicide and self-harm.We present a public health model that articulates how these social determinants might operate and how they might interact with individual-level risk factors.We then describe how these social determinants might be addressed by a whole-of-government approach involving cross-sectoral action and genuine social participation and empowerment of people with lived experience of suicide and self-harm.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.135
GPT teacher head0.393
Teacher spread0.258 · 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