Teaching Empathy Through Qualitative Research in Dental & Medical Health Promotion Education: A Snapshot
Bibliographic record
Abstract
Through this article, I endeavour to reflect on decades of experience as an applied philosophical hermeneutic qualitative researcher, engaging participants’ perspectives about living with chronic pain, osteoporosis, cancer as well as communicative approaches to medical/dental education. Having spent over a decade as an educator on the topics of professionalism empathy and ethical behaviours for dentistry students. As a co-instructor for , where whole person care and empathy become invaluable concepts to help develop these characteristics in our students. This observation of the curriculum reveals that these sometimes are referred to as “soft skills” are interspersed within the curriculum rather than interwoven throughout both classroom teaching and clinical experiences. Whereas the scientific/professional skills dentists need to demonstrate dominate the student’s experiences as they move through the curriculum. The question may become, can the two modes of professional dental/medical education co-exist in ways where both are valued and meaningfully integrated together? If so, perhaps how we teach as well as what we teach needs to find an empathic harmony. This article was written with the intention to open-up conversations among educators and students about how to teach empathy through qualitative research.
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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 | 0.068 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".