Impact of a debate on pharmacy students' views of online pharmacy practice
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of a debate on pharmacy students' perceptions, using online pharmacy practice as the debate topic. METHODS: This is a quasi-experimental interrupted time-series study. A 60 min debate was organized as a lunchtime meeting. A four-category Likert scale questionnaire (fully agree, partially agree, partially disagree, fully disagree) measured the debate participants' level of agreement with 25 statements (main issues associated with online pharmacy) in the pre-phase (before the debate), post-phase 1 (after the debate) and post-phase 2 (6 months after the debate). One hundred and seventy-seven students were recruited (response rate of 100% in the pre-phase and post-phase 1, 31% in post-phase 2). Four questions measured the perceptions of the students on this pedagogical technique. KEY FINDINGS: The overall proportion of respondents in favour of online pharmacy practice showed little variation among the three phases. However, on average (mean ± SD) 43 ± 8% of the respondents changed their opinion, 21 ± 7% reversed their opinion, 22 ± 4% nuanced their opinion and 1 ± 1% radically changed their opinion. Respectively 98% (post-phase 1) and 96% (post-phase 2) of the respondents were of the opinion that debate was a very useful teaching formula in their pharmacist training and 79 and 66% thought debate significantly changed their opinion of the issue. CONCLUSIONS: Few data have been collected on the use of debates as part of healthcare professional training. The impact of a debate on how pharmacy students feel about online pharmacy practice is described.
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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 | 0.025 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".