Striking fear into students’ hearts: Unforeseen consequences of prescribing education
Bibliographic record
Abstract
PURPOSE: Undergraduate medical education (UGME) has to prepare students to do safety-critical work (notably, to prescribe) immediately after qualifying. Despite hospitals depending on them, medical graduates consistently report feeling unprepared to prescribe and they sometimes harm patients. Research clarifying how to prepare students better could improve healthcare safety. Our aim was to explore how students experienced preparing for one of their commonest prescribing tasks: intravenous fluid therapy (IVFT). METHODS: Complexity assumptions guided the research, which used a qualitative methodology oriented towards hermeneutic phenomenology. The study design was an uncontrolled and unplanned complex intervention: judicial review of the iatrogenic death of five children due to hyponatraemia in our region had resulted in the recommendation that students' education in 'the implementation of important clinical guidelines' relevant to fluid and electrolyte balance should be intensified. An opportunity sample of 40 final-year medical students drew and gave audio-recorded commentaries on rich pictures. We completed two template analyses: one of participants' transcribed commentaries on the pictures and one using a novel heuristic to analyse the pictures themselves. We then reconciled the two analyses into a single template. RESULTS: There were four themes: affects, teaching and learning, contradictions, and the curriculum as a journey. To explore interconnections between themes, we chose the picture best exemplifying each of the four themes and interpreted the curriculum journey depicted in each of them. These interpretations were grounded in each participant's picture, verbal account of the picture, and the aggregate findings of the template analysis. Participants' experiences were influenced by the situated complexity of IVFT. Layered on top of that, contradictions, overlaps, and gaps within the curriculum introduced extraneous complexity. Confusion and apprehension resulted. CONCLUSIONS: After spending five years preparing to prescribe IVFT, participants felt unprepared to do so. We conclude that intensive teaching had not achieved its avowed goal of improving students' preparedness for safe practice. Merton's seminal work on the 'unanticipated consequences of purposive social action' suggests that intensive teaching may even have contributed to their unpreparedness.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| 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".