The reliability characteristics of the REFLECT rubric for assessing reflective capacity through expressive writing assignments: A replication study
Bibliographic record
Abstract
INTRODUCTION: The medical education community has implemented writing exercises that foster critical analysis and nurture reflective capacity. The REFLECT rubric (Wald et al. 2012) was developed to address the challenge of assessing these written reflections. The objective of this replication work is to explore the reproducibility of the reliability characteristics presented by the REFLECT developers. METHODS: Five raters evaluated narratives written by medical students and experienced clinicians using the REFLECT rubric. Reliability across rubric domains was determined via intraclass correlation coefficient and internal consistency was determined via Cronbach's alpha. RESULTS: Intraclass coefficients demonstrated poor reliability for ratings across all tool criteria (0.350-0.452) including overall ratings of narratives (0.448). Moreover, the internal consistency between scale items was also poor across all criteria (0.529-0.621). DISCUSSION: We did not replicate the reliability characteristics presented in the original REFLECT article. We consider these findings with respect to the contextual differences that existed between our study and the Wald and colleagues study, pointing particularly at the possible influence that repetitive testing and refinement of the tool may have had on their reviewers' shared understanding of its use. We conclude with a discussion about the challenges inherent to reductionist approaches to assessing reflection.
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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 | 0.179 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".