Training Socially-Conscious Dentists: Development and Integration of Community Service-Learning in Dental Curricula in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Commuity-centred education models of care are needed to address the high dental treatment needs of equity-deserving populations with a focus on person-centred care and learner-centred education. The Community Service-Learning (CSL) programme was developed and integrated into the undergraduate dental curriculum at the Schulich School of Medicine and Dentistry in Canada to train undergraduate dental learners with a curriculum reflective of the complex and evolving oral health needs of local equity-deserving communities and global populations. METHODS: The CSL programme was developed utilizing Yoder's framework for service-learning and Lave and Wenger's framework for situated learning. Community consultations were held with partner organizations and their service users in Ontario and East Africa to engage them in the development and implimentation of the CSL programme. Learners' feedback was sought through an anonymous self-reported survey to gauge their overall experiences and perceptions about the newly implemented CSL programme. RESULTS: Seven community sites were selected in London, Ontario for the third-year mandatory placements. Eight learners rotated through each site. All fourth-year learners rotated through one community site in Woodstock, Ontario. Overall, the CSL programme received positive feedback from all the learners who participated in the survey with an emphasized desire for the CSL program to continue. Three major strengths emerged from the learners' perceptions of the programme: 'the importance of community outreach and access to care', 'educational and professional development', and 'strengthening of community relationships'. Perceived areas for improvement included: 'patient accessibility and communication' and, 'enhanced exposure and opportunities for the CSL programme'. CONCLUSION: The CSL programme provided positive experiential learning opportunities for learners while addressing the oral health needs of equity-deserving community members in local and global contexts. CLINICAL RELEVANCE: This case study provides a practical and evidence-based approach in developing an experiential learning programme that trains dental learners to overcome challenges associated with oral health access and to address the unmet oral health needs of equity-deserving communities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".