To be and not to be: the paradox of the emerging professional stance
Bibliographic record
Abstract
PURPOSE: Understanding how students resolve professional conflict is essential to teaching and evaluating professionalism. The purpose of this study was to refine an existing coding structure of rationalizations of student behaviour, and to further our understanding of students' reasoning strategies in the face of perceived professional lapses. METHODS: Anonymous essays were collected from final year medical students at two universities. Each essay included a description of a specific professional lapse, and a consideration of how the lapse was dealt with. Essays were analysed using grounded theory. The resulting coding structure was applied using NVivo software. RESULTS: Twenty essays, containing 147 instances of articulated reasoning, were included. Three major categories (and several subcategories) of reasoning strategies emerged: Narrative Attitude (deflection or reflection), Dissociation (condescension or identity mobility), and Engagement (with associated action or no action). This data set revealed a wider range of Narrative Attitude than in the original study, confirmed the dominance of Dissociation as a reasoning strategy, and, perhaps paradoxically, also revealed significant evidence of action on the part of the students (predominantly directed towards dealing with the consequences of a lapse or confronting the lapser). Most of these actions were perceived to be ineffective. CONCLUSIONS: Encountering a professional lapse can be a paradoxical and profoundly disordering experience for students. When students report these experiences, they invoke reasoning strategies that enable them to re-story the lapse. Their methods of re-storying provide insight into the double-binds that students experience, their efforts to transcend these double-binds, and, through these, their emerging professional stance.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".