Oral health and access to dental care: a qualitative exploration in rural Quebec.
Bibliographic record
Abstract
INTRODUCTION: We sought to explore how rural residents perceive their oral health and their access to dental care. METHODS: We conducted a qualitative research study in rural Quebec. We used purposeful sampling to recruit study participants. A trained interviewer conducted audio-recorded, semistructured interviews until saturation was reached. We conducted thematic analysis to identify themes. This included interview debriefing, transcript coding, data display and interpretation. RESULTS: Saturation was reached after 15 interviews. Five main themes emerged from the interviews: rural idyll, perceived oral health, access to oral health care, cues to action and access to dental information. Most participants noted that they were satisfied with the rural lifestyle, and that rurality per se was not a threat to their oral health. However, they criticized the limited access to dental care in rural communities and voiced concerns about the impact on their oral health. Participants noted that motivation to seek dental care came mainly from family and friends rather than from dental care professionals. They highlighted the need for better education about oral health in rural communities. CONCLUSION: Residents' satisfaction with the rural lifestyle may be affected by unsatisfactory oral health care. Health care providers in rural communities should be engaged in tailoring strategies to improve access to oral health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".