Social Justice Education in Dentistry: A Qualitative Analysis and Conceptual Framework
Bibliographic record
Abstract
BACKGROUND: Social justice, empathy, and social responsibility are emerging themes in dentistry. Many dental faculties have started incorporating these concepts into their curriculum, but our knowledge of the effectiveness of these initiatives remains limited. PURPOSE: The objective of this study was to understand how students and educators perceive the role of social justice education, if any, in the undergraduate dentistry program. METHODS: This qualitative study was performed by using semistructured interviews with students, professors, and clinical faculty at the Université de Montréal dental school from January to May 2020. Eighteen participants were recruited through purposeful sampling until saturation. The interviews were recorded and transcribed integrally. They were coded with QDA Miner 5.0 (Provalis). Thematic analysis was undertaken to elucidate emerging themes via qualitative methodology. RESULTS: Five themes emerged from the results. Certain students were more inclined to be interested in social justice and participate in voluntary community-based activities. There were gaps in current teaching methods, including a lack of exposure to alternative treatment plans (e.g., extractions vs. endodontic treatments) better suited to patients' financial or social situations. Some barriers to teaching were identified, the most important being a lack of time. Desired teaching of social justice would include increased awareness and active student participation and taking responsibility to motivate action to produce social change. The application of social justice in dentistry involves care that is accessible and adapted to a patient's individual needs. CONCLUSION: The results of this study provide valuable insight for the development of a social justice education curriculum in dentistry that can be evaluated and validated to train socially competent dentists who can provide patient-centered care to the community. KNOWLEDGE TRANSFER STATEMENT: The results of this study can be used by dental educators and administrators who are looking to incorporate social justice education into their dental school undergraduate curriculum. The findings serve as a starting point to foster discussions and inspire change to reduce inequalities within the dental health care system.
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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 | 0.028 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".