Exploring the impact of suicide care experiences and post-intervention supports sought among community pharmacists: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: There is a need to appropriately train, support and remunerate pharmacists for their expanding roles in mental healthcare. Pharmacists often care for people experiencing mental health crises, including suicidal thoughts and behaviours, but little is known about pharmacists' suicide care experiences. AIM: This cross-sectional study aimed to explore the impact of professional experiences with people at risk of suicide and support accessed, among community pharmacists. METHOD: A survey exploring pharmacists' experiences with people at risk of suicide and post-intervention support-seeking was disseminated through Australian and Canadian professional associations, conferences and social media (June 2016-May 2017). Quantitative data were analysed using Chi-squared, Fisher's exact and independent t-tests, where appropriate. Qualitative data exploring the impact of these experiences were thematically analysed, and reasons for not seeking help post-intervention were identified. RESULTS: Among 378 respondents, 84% had encountered patients at risk of suicide and 28% had lost patients to suicide. Some were negatively affected personally and/or professionally (11%), of which 88% did not seek professional support, mainly due to uncertainty about available services. Pharmacists were significantly more negatively affected if they had a personal mental health diagnosis (p = 0.017) and previous suicide care experiences (p = 0.001). Qualitative themes included: expanding knowledge and skills, role limitation and emotional impact and response. CONCLUSION: A large proportion of pharmacists have interacted with suicidal patients and are impacted by these experiences, yet few seek help due to lack of awareness and access. There is a need to recognize pharmacists' roles in suicide care, and develop pharmacist-specific post-intervention support.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".