Task‐ versus ego‐oriented feedback delivered as numbers or comments during intubation training
Bibliographic record
Abstract
CONTEXT: Learners can focus on mastery (i.e. task orientation) or on learning relative to others (i.e. ego orientation). Previous research suggests task orientations are optimal for learning, a benefit usually linked to the suggestion that qualitative comments are better for learning than quantitative comparisons (i.e. grades). Yet, it is not clear if the observed effects are attributable to the feedback orientation (i.e. task versus ego), feedback format (i.e. comments versus numerical scores), or an interaction between the two. Here, we aimed to clarify the effects of feedback orientation and feedback format during simulation-based training in endotracheal intubation. METHODS: Forty-one medical students were randomly assigned to four feedback conditions: Task-oriented Numerical; Ego-oriented Numerical; Task-oriented Comment, and Ego-oriented Comment. Participants performed a total of 20 trials of endotracheal intubation. Pre-test, post-test and retention test assessments included the use of hand motion analysis and a global rating scale (GRS). Participants rated feedback credibility, self-assessment and self-confidence using Likert-type scales. Analyses of variance were used to examine group differences. RESULTS: Participants' performance did not differ significantly on the easiest (p > 0.2) or two complex variations of intubation (p > 0.3). For the moderately difficult variation, analyses of hand motion and GRS data revealed significant group differences at post-test (p < 0.05), but no differences on the retention test. Analysis of participants' perceptions showed significant interactions whereby the Ego-oriented Numerical group rated feedback credibility (p < 0.01) higher than the Task-oriented Numerical group; the two Comment groups did not differ. Some participants recounted negative experiences with the feedback they received. CONCLUSIONS: Medical students responded to feedback in ways that challenge previous education research. Specifically, students preferred and improved more in the short term (but not at retention) when receiving Ego-oriented feedback in Numerical form. Although learning retention did not differ significantly across feedback conditions, students' perceptions of themselves and of the teacher and training environment did differ and the implications for trainees' future learning must be considered.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".