Using formative feedback to teach pharmacy students to write critical self-reflections
Bibliographic record
Abstract
Critical thinking is an essential skill for practising pharmacists. Critical self-reflections have been said to contribute to critical thinking, and formative feedback has been cited as improving student learning. We conducted a two-year study, using mixed methods research, with two cohorts of 1st to 4th year pharmacy students, 76 students in total, to improve their critical self-reflective skills as part of critical thinking. Our pedagogical approaches, unlike the pedagogy of didactic teaching that is historically associated with health professional education, included the writing of critical self-reflections and the use of formative assessment. Students wrote eight self-reflections over four semesters. We provided formative feedback after each self-reflection, which included individual written comments and whole class formative feedback during an explicit instruction class. However, to strengthen the study design we did not provide any feedback between the first and second reflections. Quantitative analysis using a rating scale for the components of knowledge, self-assessment, and critical thinking indicated that students improved in their self-reflections and in their critical thinking, although there were differences between the two cohorts. Qualitative analysis revealed that many students valued the formative feedback and thought it improved their learning, but not all students agreed. We suggest that these pedagogical approaches can cross disciplines.
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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 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".