Understanding the Factors that Affect the Assessment of Student Performance in Pharmacy Practicums
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to explore factors that may influence a practice educator's assessment of an entry-to-practice pharmacy student during inpatient direct patient care practicums. METHODS: This was a qualitative analysis of semi-structured interviews of existing practice educators from a variety of hospital practice environments. Participants were asked to assess a fictional case of a student's work, which provided a framework for a broader discussion of assessment practices. Interviews were transcribed and subjectively analyzed for themes and factors that each study participant considered for the case and in their past precepting experiences. RESULTS: A total of 13 participants consented and were interviewed. Identified themes included the quality of student work, key aspects of the student's performance, professionalism, and the complexity of the patient assigned to the student. There was significant heterogeneity in both the assessment of the fictional student and the factors that influenced each participant's assessment. It was clear that not all guidance provided by the academic institution is read or followed. Participants described challenges in applying the assessment rubric, including lack of time, training, resources, knowledge of the degree requirements/structure, and psychological factors inherent in high-stakes courses. CONCLUSION: Complex, varied, and often contradictory factors are used by experiential practice educators in their assessment of pharmacy students on practicum. These findings lead to inconsistency and heterogeneity when assessing a mock case and actual students. Educators should consider mandatory training to ensure those who assess students have an understanding of practicum course expectations and assessments.
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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 | 0.022 | 0.112 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".