Beliefs about managing dental problems among older people and dental professionals in Southern Brazil
Bibliographic record
Abstract
OBJECTIVES: To explore the sociocultural context in which patients and dentists in urban and rural communities in Southern Brazil interpret dental problems. METHOD: Beliefs and experiences related to dental problems were explored in eight focus groups involving a total of 41 older patients, and in direct interviews with two dentists and two dental assistants. The interactions were audio recorded and transcribed for thematic analysis. RESULTS: The beliefs and experiences of the participants focused on four main themes: cultural beliefs; dental services; decisions to extract teeth; and expectations for change. A culture of pre-nuptial tooth loss and complete dentures was considered beneficial to young women. Although dental services at the time were scarce in the region, demands for relief of pain were extensive despite the fear and anxiety of the participants. Extraction of teeth and fabrication of complete dentures were the usual dental treatments available, although some participants felt that dentists withheld other treatment options. Participants were hopeful that dental services would improve for their children. CONCLUSIONS: Patients and dental professionals in urban and rural communities of Southern Brazil managed dental problems within a culture of limited access and availability of services that favoured dental extractions and complete dentures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".