Know When to Rock the Boat: How Faculty Rationalize Students’ Behaviors
Bibliographic record
Abstract
BACKGROUND: When faculty evaluate medical students’ professionalism, they make judgments based on the observation of behaviors. However, we lack an understanding of why they feel certain behaviors are appropriate (or not). OBJECTIVE: To explore faculty’s reasoning around potential student behaviors in professionally challenging situations. DESIGN: Guided interviews with faculty who were asked to respond to 5 videotaped scenarios depicting students in professionally challenging situations. SUBJECTS: Purposive sample of 30 attending Internists and surgeons. APPROACH: Transcripts were analyzed using modified grounded theory to search for emerging themes and to attempt to validate a previous framework based on student responses. RESULTS: Faculty’s reasoning around behaviors were similar to students’ and were categorized by three general themes: Imperatives (e.g., take care of patients, behave honestly, know your place), Affect (factors relating to a student’s “gut instincts” or personality), or Implications (for the student, patients, and others). Several new themes emerged, including “know when to fudge the truth”, “do what you’re told”, and “know when to step up to the plate”. These new codes, along with a near ubiquitous reference to Affect, suggests that faculty feel students are responsible for knowing when (and how) to bend the rules. Potential reasons for this are discussed. CONCLUSIONS: Although faculty are aware of the conflicts students face when encountering professional challenges, their reliance on students to “just know” what to do reflects the underlying complexity and ambiguity that surrounds decision making in these situations. To fully understand professional decision-making, we must acknowledge and address these issues from both students’ and faculty’s points of view.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".