Evaluating reflective writing to guide curricular improvements in health informatics education
Bibliographic record
Abstract
The use of reflective essays to guide curricular improvements is explored in this qualitative study of two cohorts of students enrolled in a graduate health informatics course. The research questions, methods, and analysis were co-designed with the course instructor and a student who had completed the course and was a teaching assistant in the subsequent year. We thematically analyzed 95 anonymized student reflective essays with a taxonomy of learning and codes developed using the assignment rubric and similar themes derived from published literature. Major themes that emerged were (1) foundational knowledge acquisition and understanding, (2) integration and critical reflection, (3) self-discovery and imagining possibilities beyond the course, and (4) sharing and the human dimension. The results reinforce strengths of the course and informs areas for curricular improvement that incorporate reflection as part of a summative assessment. The discussion highlights challenges students had in writing reflective essays, which will be used to refine the activity and the instructions for the assignment. Our findings will assist students in developing their reflective practice to carry forward in their experiential internship in the program and their careers.
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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 | 0.090 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".