Bibliographic record
Abstract
PURPOSE: To develop a deeper understanding of the complexity of physicians' decision making when faced with professional challenges. METHOD: Using constructivist grounded theory, the authors conducted a secondary analysis of transcripts from focus groups with 40 internists in 2011. Participants responded to scripted professional challenge scenarios. The authors analyzed the transcripts for instances in which participants discussed "doing what might be wrong" (i.e., something that goes against their values or others' expectations). They used the theory of planned behavior (TPB), which posits that intention to act is predicted by attitudes, subjective norms, and perceived behavioral control, to understand the findings in a broader context. RESULTS: The theme of "doing what might be wrong" was pervasive, particularly in response to scenarios involving stewardship, nonpatients' requests for advice or care, or requests for e-mail access. Participants' rationales for suggested behaviors included a desire to keep patients happy and be (or appear) helpful. Modifiers of those responses included type of patient, physician's relationship with the patient, and comfort level with the request. Consistent with the TPB, attitudes or beliefs about the intended behavior, subjective norms, and perceived behavioral control influenced decision making. CONCLUSIONS: Physicians often do what might be wrong when they are asked to do something that goes against their values and beliefs, by patients, others, or as perceived by their organizations. Actions are often rationalized as being done for the right reasons. These findings should inform the development of educational initiatives to support physicians in acting in accordance with their ideals.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".