Incident‐Based Storytelling in Service‐Learning: Dental Learners’ Experiences and Key Insights
Bibliographic record
Abstract
BACKGROUND: Community service-learning (CSL) is increasingly used in education to promote oral health equity and social responsibility; however, few studies have examined how reflective incident-based storytelling narratives shape learner development within CSL contexts. This study aimed to explore how an incident-based storytelling reflective approach can contribute to the learning and professional development of undergraduate dental learners at the Schulich School of Medicine and Dentistry, Western University in Canada. . METHODS: Kolb's experiential learning theory (1984) guided the development of a structured, dynamic approach to writing incident-based storytelling narratives within the CSL program. After completing their CSL placements, learners submitted reflective essays based on this framework. Forty-nine essays were thematically analyzed using an inductive interpretive approach. RESULTS: Three major themes emerged from the learners' reflective essays: (1) Barriers to accessing oral healthcare, (2) community-integrated care, and (3) personal and professional development. By recounting specific incidents and patient encounters, learners expressed increased awareness of the social determinants of health, and reflected on how these experiences deepened their understanding of cultural humility, empathy, and professional development. CONCLUSIONS: Integrating incident-based storytelling narratives may contribute towards deeper understating of empathy and cultural humility, including barriers to dental care. These findings highlight the learners' immediate perceptions during CSL and highlight the potential of narrative reflection in dental profession as a transformative pedagogical tool in dental education.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".