Pharmacists' participation in research: a case of trying to find the time
Bibliographic record
Abstract
Objective The objective of this case study was to explore how pharmacists involved in the Pharmacy Study Of Natural Health Product Adverse Reactions (SONAR) project perceived the barriers and facilitators to participating in clinical research. Methods A total of 19 semi-structured interviews were completed with pharmacy staff members who had recently completed data collection in the SONAR study which involved asking patients if they had experienced any unwanted effects while taking natural products. Other data sources included detailed field notes and interviews with SONAR researchers. Basic content analysis with multiple coders was used to analyse the data and triangulation was used to highlight areas of consistency and contrasting view points across the data types. Key findings None of the participating pharmacies was able to collect as much data as expected by the SONAR team. Lack of time was stated as the main reason why pharmacy staff had trouble with the data collection. However, observational data and detailed probing in interviews confirmed that data collection itself took very little time (seconds per patient). Lack of time was provided as a socially acceptable excuse that masked deeper issues related to fears associated with challenges modifying established work routines and perceived lack of value associated with research participation. Conclusion To successfully engage pharmacists in practice-based natural health product research it is necessary to establish the direct and indirect benefits of participation because those that believe in the value of the research will make the time for participation.
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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 | 0.063 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".