Challenges in multisource feedback: intended and unintended outcomes
Bibliographic record
Abstract
CONTEXT: Multisource feedback (MSF) is a type of formative assessment intended to guide learning and performance change. However, in earlier research, some doctors questioned its validity and did not use it for improvement, raising questions about its consequential validity (i.e. its ability to produce intended outcomes related to learning and change). The purpose of this qualitative study was to increase understanding of the consequential validity of MSF by exploring how doctors used their feedback and the conditions influencing this use. METHODS: We used interviews with open-ended questions. We purposefully recruited volunteer participants from 2 groups of family doctors who participated in a pilot assessment of MSF: those who received high (n = 25) and those who received average/lower (n = 44) scores. RESULTS: Respondents included 12 in the higher- and 16 in the average/lower-scoring groups. Fifteen interpreted their feedback as positive (i.e. confirming current practice) and did not make changes. Thirteen interpreted feedback as negative in 1 or more domains (i.e. not confirming their practice and indicating need for change). Seven reported making changes. The most common changes were in patient and team communication; the least common were in clinical competence. Positive influences upon change included receiving specific feedback consistent with other sources of feedback from credible reviewers who were able to observe the subjects. These reviewers were most frequently patients. DISCUSSION: Findings suggest circumstances that may contribute to low consequential validity of MSF for doctors. Implications for practice include enhancing procedural credibility by ensuring reviewers' ability to observe respective behaviours, enhancing feedback usefulness by increasing its specificity, and considering the use of more objective measures of clinical competence.
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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 | 0.388 | 0.740 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".