Pharmacists’ role and experiences with delivering mental health care within team-based primary care settings during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: Pharmacists have been increasingly integrated into primary care teams, leading to improved health outcomes for patients. The two objectives of this study were (i) to describe how the COVID-19 pandemic impacted pharmacists' role in mental health care within Canadian primary care teams and (ii) to describe Canadian pharmacists' experiences collaborating with other healthcare providers in the delivery of mental health services during the COVID-19 pandemic. METHODS: Cross-sectional observational study utilizing an online survey consisting of closed-ended and open-ended questions. Primary care pharmacists in Ontario were eligible to participate. Descriptive statistics were collated, and qualitative data underwent thematic analysis. A total of 51 pharmacists participated in the study. KEY FINDINGS: The COVID-19 pandemic has led to the expanding role of pharmacists in attending to the mental health care of patients. Working within a collaborative, interprofessional healthcare environment, pharmacists support patients' mental health in a variety of ways, including medication education and management, non-pharmacologic approaches and supportive conversations, and identification of resources, including referrals, wellness checks, and consulting with physicians. Increasing demand for mental health services has led to higher referrals to pharmacists, which will likely persist and require further education of pharmacists in mental health along with better access to deliver virtual care. CONCLUSION: In response to the increasing mental health care needs of patients since the COVID-19 pandemic, primary care pharmacists reported increased attention spent on mental health care. Building capacity and ensuring support for pharmacists to continue to address the increasing mental health care demands is essential.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".