Residents' perceptions of what makes feedback valuable in workplace‐based learning: A discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: Research on feedback has shifted emphasis away from its 'delivery' to consideration of the interaction between individual learners and their 'feedback provider'. The complexity inherent in determining whether feedback is perceived as valuable by learners, however, can quickly overwhelm educators if every interaction must be considered completely idiosyncratic. We, therefore, require a better understanding of variability in the ways in which feedback is perceived. To that end, we ran a discrete choice experiment aimed at determining residents' preferences and whether discernible patterns exist across learners regarding factors that influence perceptions of feedback's learning value. METHODS: We performed a discrete choice experiment in which respondents were asked to read a clinical case and select repetitively between two feedback scenarios that differed according to six attributes identified from the literature as influencing feedback credibility: Dialogue, Focus, Relationship, Situation, Source and Valence. By systematically varying the levels of each attribute contained in the scenarios and asking residents to choose which from each pair they deemed more valuable for learning, a mixed logit model and latent class analysis could be applied to determine learners' feedback preferences and whether clusters of preference exist. RESULTS: Ninety-five elderly care medicine residents in the Netherlands completed the questionnaire. Their responses indicated that Valence, Dialogue, Relationship and Focus each accounted for about 20% of their preferences regarding the type of feedback perceived to offer the most learning value. Source and Situation were less influential, each accounting for 11% of the choices made. A latent class model with three clusters of respondents best accounted for the heterogeneity in feedback preferences. A total of 62% of respondents could be assigned to one of the three profiles with at least 80% probability. None of the respondents' characteristics (seniority, residency programme nor sex) were related to the feedback preference profile. DISCUSSION: Our findings suggest that 'how' feedback is provided has a greater influence on perceived learning value than who provides it. That said, variability exists in resident perceptions with no evidence (as yet) of factors that predict individual preferences. As such, tailoring to the needs and reactions of individual learners is likely to require open and ongoing conversations, and we recommend using the learner profiles generated through this study as a starting point because they provide classifications that could facilitate effective connections for the majority of residents.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".