Understanding responses to feedback: the potential and limitations of regulatory focus theory
Bibliographic record
Abstract
OBJECTIVES: Regulatory focus theory posits the existence of two systems of self-regulation underlying human motivation: promotion focus, which is concerned with aspirations and accomplishments, and prevention focus, which is concerned with obligations and responsibilities. It has been proposed that regulatory focus theory may help to explain learners' variable responses to feedback, predicting that positive feedback is motivating under promotion focus, whereas negative feedback is motivating under prevention focus. We aimed to explore this link between regulatory focus theory and response to feedback using data collected in a naturalistic setting. METHODS: In a constructivist grounded theory study, we interviewed 22 early-career academic doctors about experiences they perceived as influential in their learning. Although feedback emerged as important, responses to feedback were highly variable. To better understand how feedback becomes (or fails to become) influential, we used the theoretical framework of regulatory focus to re-examine all descriptions of experiences of receiving and responding to feedback. RESULTS: Feedback could be influential or non-influential, regardless of its sign (positive or negative). In circumstances in which the individual's regulatory focus was readily determined, such as in choosing a career (promotion) or preparing for a high-stakes examination (prevention), the apparent influence of feedback was consistent with the prediction of regulatory focus theory. However, we encountered many challenges in applying regulatory focus theory to real feedback scenarios, including the frequent presence of a mixed regulatory focus, the potential for regulatory focus to change over time, and the competing influences of other factors, such as the perceived credibility of the source or content of the feedback. CONCLUSIONS: Regulatory focus theory offers a useful, if limited, construct for exploring learners' responses to feedback in the clinical setting. The insights and predictions it offers must be considered in light of the motivational complexity of clinical learning tasks and of other factors influencing the impact of feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".