The willingness of community pharmacists to participate in a practice-based research network
Bibliographic record
Abstract
BACKGROUND: Practice-based research networks (PBRNs) are groups of practitioners and researchers with an interest in designing, evaluating and disseminating solutions to the real-world problems of clinical practices. OBJECTIVE: To evaluate the level of interest of community pharmacists in participating in a PBRN and to document the services such a network should offer. METHOD: In a survey of community pharmacists in Montreal, Quebec, and surrounding areas, a questionnaire was mailed to a random sample of 1250 pharmacists. Two of the 28 questions were related to PBRNs: one assessed the pharmacists' interest in participating in a PBRN; the other sought their views on which services and activities this network should offer. RESULTS: In total, 571 (45.7%) pharmacists completed the questionnaire, but 6 did not answer the questions about the PBRN. Of the respondents, 58.9% indicated they were "very interested" or "interested" in joining a PBRN, while 41.1% reported little or no interest. The most popular potential services identified were access to clinical tools developed in research projects (77.0%), access to continuing education training programs developed in research projects (75.9%), information about conferences on pharmacy practice research (64.1%) and participation in the development of new pharmaceutical practices (56.1%). CONCLUSION: This study suggests that the level of interest that community pharmacists have in PBRNs is sufficient to further evaluate how such networks may optimize and facilitate pharmacy practice research. Can Pharm J 2013;146:47-54.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".