A scoping review of community pharmacists and patients at risk of suicide
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".