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Record W4292852492 · doi:10.1016/j.ienj.2022.101199

Assessment and care of individuals at risk of suicide in Emergency Department: The SecUrgence protocol

2022· article· en· W4292852492 on OpenAlexafffund
Jessica Rassy, Alain Lesage, Réal Labelle, Farida Saadi, Marie‐Hélène Goulet, Christine Genest, Nathalie Maltais, Caroline Larue

Bibliographic record

VenueInternational Emergency Nursing · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversité du Québec à RimouskiCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalQuebec Network for Research on AgingHôpital Maisonneuve-RosemontInstitut universitaire en santé mentale de MontréalUniversité de MontréalInstitut Universitaire en Santé Mentale de QuébecHôpital Charles-Le MoyneUniversité du Québec à MontréalUniversité de Sherbrooke
FundersRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsDelphi methodEmergency departmentProtocol (science)MedicineMedical emergencyDelphiSuicide preventionPoison controlFamily medicinePsychologyNursingAlternative medicineComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.037
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.055
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0060.002
Scholarly communication0.0030.003
Open science0.0030.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0180.005

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 head0.075
GPT teacher head0.494
Teacher spread0.418 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes2
Has abstractno

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