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Record W2793502048 · doi:10.1177/0706743717753147

Media Guidelines for Reporting on Suicide: 2017 Update of the Canadian Psychiatric Association Policy Paper

2018· article· en· W2793502048 on OpenAlexaffvenueabout
Mark Sinyor, Ayal Schaffer, Marnin J. Heisel, André Picard, Gavin Adamson, Christian P. Cheung, Laurence Y. Katz, Rakesh Jetly, Jitender Sareen

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of ManitobaCanadian Armed ForcesHealth Sciences CentreSunnybrook Health Science CentreLawson Health Research InstituteDalhousie UniversityToronto Metropolitan UniversityUniversity of TorontoWestern University
Fundersnot available
KeywordsPsychiatryAssociation (psychology)Suicide preventionPoison controlOccupational safety and healthHuman factors and ergonomicsInjury preventionMedicinePsychologyMedical emergency

Abstract

fetched live from OpenAlex

This paper has been substantially revised by the Canadian Psychiatric Association’s Research Committee and approved for republication by the CPA’s Board of Directors on May 3, 2017. The original policy paper 1 was developed by the Scientific and Research Affairs Standing Committee and approved by the Board of Directors on November 10, 2008.

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)0.183
metaresearch head score (Gemma)0.375
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1830.375
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0320.027
Science and technology studies0.0140.010
Scholarly communication0.0220.011
Open science0.0220.009
Research integrity0.0280.028
Insufficient payload (model declined to judge)0.0290.018

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 head0.084
GPT teacher head0.378
Teacher spread0.294 · 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.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations62
Published2018
Admission routes3
Has abstractyes

Explore more

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