Factors influencing responsiveness to feedback: on the interplay between fear, confidence, and reasoning processes
Bibliographic record
Abstract
Self-appraisal has repeatedly been shown to be inadequate as a mechanism for performance improvement. This has placed greater emphasis on understanding the processes through which self-perception and external feedback interact to influence professional development. As feedback is inevitably interpreted through the lens of one's self-perceptions it is important to understand how learners interpret, accept, and use feedback (or not) and the factors that influence those interpretations. 134 participants from 8 health professional training/continuing competence programs were recruited to participate in focus groups. Analyses were designed to (a) elicit understandings of the processes used by learners and physicians to interpret, accept and use (or not) data to inform their perceptions of their clinical performance, and (b) further understand the factors (internal and external) believed to influence interpretation of feedback. Multiple influences appear to impact upon the interpretation and uptake of feedback. These include confidence, experience, and fear of not appearing knowledgeable. Importantly, however, each could have a paradoxical effect of both increasing and decreasing receptivity. Less prevalent but nonetheless important themes suggested mechanisms through which cognitive reasoning processes might impede growth from formative feedback. Many studies have examined the effectiveness of feedback through variable interventions focused on feedback delivery. This study suggests that it is equally important to consider feedback from the perspective of how it is received. The interplay observed between fear, confidence, and reasoning processes reinforces the notion that there is no simple recipe for the delivery of effective feedback. These factors should be taken into account when trying to understand (a) why self-appraisal can be flawed, (b) why appropriate external feedback is vital (yet can be ineffective), and (c) why we may need to disentangle the goals of performance improvement from the goals of improving self-assessment.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".