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Record W2762854833 · doi:10.1177/1715163517733482

A scoping review of community pharmacists and patients at risk of suicide

2017· review· en· W2762854833 on OpenAlexvenueno aff
Andrea Murphy, Katelyn Hillier, Randa Ataya, Pierre Thabet, Anne Marie Whelan, Claire L. O’Reilly, David M. Gardner

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2017
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical emergencyFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Medications are commonly used in suicide attempts. Pharmacists are inextricably linked to medications and may have roles in helping those at risk of suicide. We conducted a scoping review to characterize the existing literature and make recommendations about future research. METHODS: We used a 6-step approach based on an existing scoping review methodological framework, including identifying the research question; identifying relevant studies and other literature; study and literature selection; data charting; collating, summarizing and reporting results; and dissemination of results. We searched electronic databases, various grey literature sources and mobile app stores. RESULTS: = 9), primarily assessing pharmacists' knowledge and attitudes. Themes included education and training to impact knowledge and attitudes, gatekeeping of medication supply, collaboration and integration, and role perception. Public perspectives on pharmacists' roles were limited. CONCLUSIONS: Research regarding pharmacists' roles in the care of people at risk for suicide is limited. The areas that have dominated the literature include legal liability, especially with respect to gatekeeping medications, ethical decision making and education and training. Research is needed to determine what methods, outcomes and measures are required to best serve in building the evidence base for policy and practice decisions 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 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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.339
GPT teacher head0.473
Teacher spread0.134 · 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.

Study designOther design
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

Citations31
Published2017
Admission routes1
Has abstractyes

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