The Roles of Community Pharmacists in Managing Patients with Diabetes: Perceptions of Health Care Professionals in Nova Scotia
Bibliographic record
Abstract
Background: Community pharmacists could reduce the clinical and economic burden of diabetes by participating in diabetes management. Pharmacists' roles in collaboration with other health care professionals should be understood before any community pharmacy—based programs focusing on diabetes management are designed and implemented. Methods: A sample of primary health care physicians, community pharmacists, and nurses and dieticians from Diabetes Centres in the Capital Health District of Nova Scotia were interviewed on their perceptions of community pharmacists' role in diabetes care. A semistructured format was used. Data were compiled and analyzed descriptively, and are reported here in terms of the roles of community pharmacists in this setting. Results: A total of 18 health care professionals were interviewed: 8 (44%) primary health care physicians, 6 (33%) community pharmacists, and 4 (22%) nurses and dieticians. All responses were analyzed, and six general roles for community pharmacists were identified, such as education about drug therapy (83% of respondents), followed by providing information to patients about glucose monitoring and healthy lifestyles (67% of respondents). Conclusion: In a pilot project ( n = 18), Nova Scotia health care professionals supported pharmacists' role in diabetic care, such as educating patients about drug therapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".