DENTAL STUDENTS' PERSPECTIVES ON RURAL DENTAL PRACTICE: A QUALITATIVE STUDY.
Bibliographic record
Abstract
INTRODUCTION: The chronic shortage of dentists in rural communities may affect the quality of care provided to these communities. The aim of this study was to explore the knowledge and perspectives of Quebec's future dentists regarding rural dental practice and their career intentions. METHODS: We conducted a qualitative study at 2 major dental faculties using the interpretive description method. Purposeful maximum variation sampling and snowball techniques were used to recruit 4th-year dental students and specialty residents as study participants. Face-to-face, semi-structured, 60-90-minute interviews were conducted and audio-recorded. Qualitative data were analyzed using a thematic approach including interview debriefing, transcript coding, data display and interpretation. RESULTS: Of the 17 interviews, 10 were with women and 7 with men; the age range of participants was 22-39 years. Five major themes emerged from the interviews: awareness of access to oral health care in rural areas, image of rurality, image of rural dental practice, perceived barriers to and perceived enablers of rural dental practice. Students said that undergraduate dental education, financial rewards, professionalism, professional support and social media may positively affect their perspective on rural dental practice. CONCLUSIONS: There is a need to implement and support strategies known to increase dental students' knowledge of rural practice and their motivation to choose rural practice. Dental educators have an essential role to play in shaping professional character and encouraging apprenticeship to meet these goals.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".