Study of Understanding Pharmacists' Perspectives on Remuneration and Transition toward Chronic Disease Management (SUPPORT-CDM): Results of an Alberta-Wide Survey of Community Pharmacists
Bibliographic record
Abstract
Background: Strong evidence supports the benefits of pharmacist intervention and chronic disease management (CDM) for patients, yet most pharmacists are not providing such services. The purpose of this study was to better understand pharmacists' perceptions of CDM and potential remuneration models. Methods: We developed and tested a web-based survey based on the issues identified in a series of focus groups involving pharmacists: current practice setting, education, remuneration models, current practice environment and implementation. An invitation to complete the survey was e-mailed to registered pharmacists and was included in a weekly newsletter to Alberta pharmacists in January 2008, with 3 subsequent reminders to complete the survey. Results: Responses from 140 pharmacists were included. Pharmacists were most interested in providing CDM for diabetes (79%), although only 49% were presently comfortable with managing diabetes. The top enablers for provision of CDM included pharmacists' desire to change their scope of practice, a supportive work environment and patient demand. The top barriers included a lack of time to engage in CDM, lack of remuneration and staffing issues. Interestingly, relatively few identified pharmacists' resistance to change and difficulty finding eligible patients (38% and 25%, respectively) as important barriers. The majority of pharmacists agreed that payment should be shared between the pharmacy and the pharmacist as a fee-for-service. The average amount pharmacists expected for this model was $44.23/service. Conclusions: Pharmacists showed interest but may lack the confidence to provide CDM services to patients. Many of the facilitators and barriers point toward the need for a sustainable remuneration model for pharmacists' clinical care. We plan to use these results to help develop such a model.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".