Establishing the measurement and psychometrics of medical student feedback literacy (IMPROVE-FL): A research protocol
Bibliographic record
Abstract
Current feedback models advocate learner autonomy in seeking, processing, and responding to feedback so that medical students can become feedback-literate. Feedback literacy improves learners' motivation, engagement, and satisfaction, which in turn enhance their competencies. However, there is a lack of an objective method of measuring medical student feedback literacy in the empirical literature. Such an instrument is required to determine the level of feedback literacy amongst medical students and whether they would benefit from an intervention. Therefore, this research protocol addresses the methodology aimed at the development of a comprehensive instrument for medical student feedback literacy, which is divided into three phases, beginning with a systematic review. Available instruments in health profession education will be examined to create an interview protocol to define medical students' feedback literacy from the perspectives of medical students, educators, and patients. A thematic analysis will form the basis for item generation, which will subsequently undergo expert validation and cognitive interviews to establish content validity. Next, we will conduct a national survey to gather evidence of construct validity, internal consistency, hypothesis testing, and test-retest reliability. In the final phase, we will distribute the instrument to other countries in an international survey to assess its cross-cultural validity. This protocol will help develop an instrument that can assist educators in assessing student feedback literacy and evaluating their behavior in terms of managing feedback. Ultimately, educators can identify strengths, and improve communication with students, as well as feedback literacy and the feedback process. In conclusion, this study protocol outlined a systematic, evidence-based methodology to develop a medical student feedback literacy instrument. This study protocol will not only apply to medical and local cultural contexts, but it has the potential for application in other educational disciplines and cross-cultural studies.
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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 | 0.147 | 0.128 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".