The patient as text: a challenge for problem‐based learning
Bibliographic record
Abstract
OBJECTIVES: To explore the values and assumptions underlying problem-based learning (PBL) cases through narrative analysis, in order to consider the ways by which paper cases may affect student attitudes and values. METHODS: Randomly chosen PBL cases from the first year curriculum at Dalhousie University medical school (n = 10) were coded by 3 independent reviewers attending to narrative components. RESULTS: The cases generally used spare, objective language, used the passive voice, eliminated agency, and employed linguistic markers to encode scepticism about patient reports. There was almost no sense of the presence of the patient as person in these cases in terms of their words, feelings, or their social and cultural context. The almost complete exclusion of the preferences and priorities of the patient was striking. CONCLUSION: The sample is small, the results only suggestive. Yet it appears that the cases used in PBL may unnecessarily, even unintentionally, encourage student detachment from the messiness of real patients' lives and emotions. Positioning a particular way of seeing - the doctor's gaze - as normative renders less visible the choices that are being made whenever an account is constructed. Including multiple voices in a case would complicate that tidy reduction of choices. Ongoing attempts to enrich the case format should be encouraged. At the same time, students may benefit from being taught the skills for critical analysis of the case itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".