Assessing the psychometric properties of Kember and Leung’s Reflection Questionnaire
Bibliographic record
Abstract
Reflective thinking is often stated as a learning outcome of baccalaureate nursing education, and as a characteristic of a competent professional; however, no consistent method exists to assess the extent to which students engage in reflective thinking. To address this need, Kember and Leung developed and tested a self-report questionnaire based on Mezirow’s conceptualisation of levels of reflective thinking. The purpose of this study was to test the psychometric properties of the Reflection Questionnaire, developed by Kember and Leung. A convenience sample (n = 538) of third-year baccalaureate nursing students from four collaborative nursing programmes in Ontario was used. Ethical approval was secured from 10 sites. Second-order confirmatory factor analyses (CFA) were used to test the factor structure of the Reflection Questionnaire. This research was part of a larger study on reflective thinking and is a first step in validating a four-level measure of reflective thinking, in educational environments, with baccalaureate nursing students. The results of the second-order CFA provide support for the construct validity of reflective thinking. Results of this study contribute to the evidence supporting the reliability and validity of the questionnaire. Nurse educators can use this information when implementing the questionnaire, and learning the extent to which students are engaging in the reflective thinking process.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".