Methodological approach to generate reflection and reflective notes
Bibliographic record
Abstract
The objective of this data note is twofold: 1) to illustrate the methodological approach used to generate guided reflections at undergraduate level aided by a patient-based vignette portraying an individual with a history of substance use and mental health disorders; 2) to provide a summary of the raw data set in the form brief educational reflections submitted anonymously by undergraduate dental and dental hygiene students. These reflections were used in our recent publication titled ‘The role of an educational vignette to teach dental students on issues of substance use and mental health disorders at the University of British Columbia: An exploratory Qualitative study. By offering the reader with a road map to generate such reflections, and a summary of the reflections themselves, we hope to engage other dental schools in planning their educational teaching activities on issues pertaining to mental health and substance use for dental and dental hygiene students as we have advocated over the years.
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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 | 0.277 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".