The Disavowed Curriculum: Understanding Student's Reasoning in Professionally Challenging Situations
Bibliographic record
Abstract
CONTEXT: Understanding students' perceptions of and responses to lapses in professionalism is important to shaping students' professional development.\nOBJECTIVE: Utilize realistic, standardized professional dilemmas to obtain insight into students' reasoning and motivations in "real time."\nDESIGN: Qualitative study using 5 videotaped scenarios (each depicting a student placed in a situation which requires action in response to a professional dilemma) and individual interviews, in which students were questioned about what they would do next and why.\nSETTING: University of Toronto.\nPARTICIPANTS: Eighteen fourth-year medical students; participation voluntary and anonymous.\nMAIN OUTCOME MEASURE: A model to explain students' reasoning in the face of professional dilemmas.\nRESULTS: Grounded theory analysis of interview transcripts revealed that students were motivated to consider specific actions by referencing a Principle (an abstract or idealized concept), an Affect (a feeling or emotion), or an Implication (a potential consequence of suggested actions). Principles were classified as "avowed" as ideals of our profession (e.g., honesty or disclosure), or "unavowed" (unacknowledged or undeclared, e.g., obedience or allegiance). Implications could also be avowed (e.g., concerning patients) or unavowed (e.g., concerning others); but students were predominantly motivated by considering "disavowed" implications: those pertaining to themselves (e.g., concern for grades, evaluations, or reputation), which are actively denied by the profession and discouraged as being inconsistent with altruism.\nCONCLUSIONS: This "disavowed curriculum" has implications for education, feedback, and evaluation. Instead of denying their existence, we should teach students how to negotiate and balance these unavowed and disavowed implications and principles, in order to help them develop their own 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".