When enough is enough: Signals used by residents to indicate receptivity to questioning
Bibliographic record
Abstract
INTRODUCTION: Questioning has, since Socrates, been touted as an effective teaching technique, but its use in health professions education is controversial due to the risk of inducing counterproductively negative trainee experiences. While much has been written on optimal methods of questioning, disconnects continue to arise between well-intentioned preceptors and how questioning is experienced. Thus, the authors explored if and how learners try signalling to preceptors when questioning leads to a positive learning experience and when it ceases to be educationally valuable. METHODS: The authors conducted semi-structured interviews with 12 senior internal medicine residents to elicit perspectives on how they try to signal their wishes to preceptors during questioning interactions. This was followed by one focus group with 5 additional participants. The methodology was constructivist grounded theory and rigour was enhanced through iterative data collection and analysis, constant comparison, and theoretical sampling. RESULTS: Signalling was confirmed to be an important concept in resident-preceptor interactions because comfort with questioning was not universally positive or negative. Rather, participants signalled their openness to questioning in context-dependent ways influenced by a variety of factors. In addition to their own signalling, participants reported recognizing and responding to signals from their juniors, peers and attendings, further highlighting the communicative nature of cues being sent. DISCUSSION: With a better understanding of the contextual factors to be considered before entering a questioning interaction and identification of cues that residents believe they offer as signals of encouraging or discouraging engagement in such interactions, attending physicians should be better able to navigate clinical teaching moments to optimize resident learning.
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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 | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".