Collaboration between family physicians and community pharmacists to enhance adherence to chronic medications: opinions of Saskatchewan family physicians.
Bibliographic record
Abstract
OBJECTIVE: To ascertain the opinions of family physicians about medication adherence in patients with chronic diseases and the role of community pharmacists in improving adherence to chronic medications, as well as their opinions on increased collaboration with pharmacists to enhance medication adherence. DESIGN: A self-administered postal survey of 19 questions, with opinions collected by ordinal (5-point Likert scale) and open responses. SETTING: Saskatchewan. PARTICIPANTS: Two hundred and eighty-six family physicians working in Saskatchewan in January 2008. MAIN OUTCOME MEASURES: Descriptive statistics of physicians' opinions on the following: medication adherence in patients with chronic diseases; their current interaction with community pharmacists; and potential collaborative strategies to promote medication adherence. RESULTS: The response rate was 39.4%. Approximately 75% of the physicians acknowledged that nonadherence to chronic medications was a problem among their patients. Medication costs and side effects were identified as the 2 most common reasons for medication nonadherence. Only one-quarter of physicians communicated regularly with community pharmacists about adherence issues; most of these physicians were rural physicians. Most physicians agreed that increased collaboration with pharmacists would improve adherence, although support for potential interactions with pharmacists varied. Concerns were expressed about time required by physicians and financial reimbursement. Physicians in practice for less than 10 years and those practising in rural areas were more willing to share clinical information and communicate with pharmacists to promote medication adherence. CONCLUSION: Saskatchewan family physicians appreciate the importance of medication nonadherence but currently seldom interact with community pharmacists on this issue. They believe that pharmacists have a role in supporting patients with medication adherence and indicate a willingness to work more collaboratively with them to promote adherence. For this type of collaboration to be effective, it appears that increased adherence-related communication between the 2 health care providers and additional health care funding are required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".