Using mock interviews to prepare pharmacy students for professional placement: Results from a pilot study
Bibliographic record
Abstract
Introduction: Mock interviews were introduced into a second-year pharmacy course with an embedded pharmacy placement. The aim was to prepare pharmacy students for interviews with possible preceptors when seeking community pharmacy placements. This study aimed to assess students’ perspectives on the impact of this activity. Methods: Second year pharmacy students (n = 35) were provided with general interview guidance and participated in mock placement interviews conducted by community pharmacists. After participating in the mock interview, students were invited to complete two online questionnaires. The first questionnaire was completed following the mock interview and the second questionnaire was completed after students had secured professional placements. Both surveys contained multiple domains including student approach to placement, perceived impact of the mock interview on confidence and preparation, application of the feedback on their real-life interview, understanding employer priorities, linkage with the curriculum and overall student satisfaction. Results: Following the mock interview, most participants (89.5%, n = 17) indicated that they felt better prepared to approach a placement preceptor and for the interview process. All participants who completed the first questionnaire (100%, n = 19) agreed that the feedback following the mock interview was helpful. After securing a placement, more than half (56.5%, n = 13) indicated that they used the skillsets developed during the mock interview when approaching a placement preceptor. Conclusion: The inclusion of mock interviews in the pharmacy curricula was found beneficial and conducive to enhanced skills and confidence in students’ career development.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".