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Record W2991505316 · doi:10.1176/appi.ps.201900244

Survey of Australian and Canadian Community Pharmacists’ Experiences With Patients at Risk of Suicide

2019· article· en· W2991505316 on OpenAlexafffundabout
Andrea Murphy, Claire L. O’Reilly, Randa Ataya, Steve Doucette, Fred Burge, Luis Salvador‐Carulla, Timothy F. Chen, Dani Himmelman, Stan Kutcher, Ruth Martin‐Misener, Alan Rosen, David M. Gardner

Bibliographic record

VenuePsychiatric Services · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsNova Scotia Health Authority
FundersIWK Health CentreUniversity of WollongongDalhousie UniversityACT Government
KeywordsLogistic regressionPreparednessSuicide preventionMedicineMental healthPsychiatryOccupational safety and healthFamily medicineInjury preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: The study's objective was to examine Canadian and Australian community pharmacists' experiences with people at risk of suicide. METHODS: A survey was developed and administered online. Countries were compared by Fisher's exact and t tests. Multivariable logistic-regression analysis was used to identify variables associated with preparedness to help someone in a suicidal crisis. RESULTS: The survey was completed by 235 Canadian and 161 Australian pharmacists. Most (85%) interacted with someone at risk of suicide at least once, and 66% experienced voluntary patient disclosure of suicidal thoughts. More Australians than Canadians had mental health crisis training (p<0.001). Preparedness to help in a suicidal crisis was negatively associated with being Canadian, having a patient who died by suicide, lacking training and confidence, and permissive attitudes toward suicide. CONCLUSIONS: Several perceived barriers impede pharmacists' abilities to help patients who voluntarily disclose suicidal thoughts. Gatekeeper and related suicide prevention strategy training for community pharmacists is warranted.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.031
GPT teacher head0.306
Teacher spread0.275 · 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 teacher head, 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

Citations32
Published2019
Admission routes3
Has abstractyes

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