Quality of Narratives in Assessment: Piloting a List of Evidence-Based Quality Indicators
Bibliographic record
Abstract
Background & Need for Innovation: Appraising the quality of narratives used in assessment is challenging for educators and administrators. Although some quality indicators for writing narratives exist in the literature, they remain context specific and not always sufficiently operational to be easily used. Creating a tool that gathers applicable quality indicators and ensuring its standardized use would equip assessors to appraise the quality of narratives. Steps taken for Development and Implementation of innovation: We used DeVellis' framework to develop a checklist of evidence-informed indicators for quality narratives. Two team members independently piloted the checklist using four series of narratives coming from three different sources. After each series, team members documented their agreement and achieved a consensus. We calculated frequencies of occurrence for each quality indicator as well as the interrater agreement to assess the standardized application of the checklist. Outcomes of Innovation: We identified seven quality indicators and applied them on narratives. Frequencies of quality indicators ranged from 0% to 100%. Interrater agreement ranged from 88.7% to 100% for the four series. Critical Reflection: Although we were able to achieve a standardized application of a list of quality indicators for narratives used in health sciences education, it does not exclude the fact that users would need training to be able to write good quality narratives. We also noted that some quality indicators were less frequent than others and we suggested a few reflections on this.
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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 | 0.620 | 0.718 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".