Dental care utilization: patterns and predictors in persons living with HIV in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: To identify the predisposing, enabling, and need factors of the Andersen and Newman (A&N) model and their associations with the pattern of dental service utilization in a sample of people living with HIV (PLHIV) in British Columbia. METHODS: Participants responded anonymously to a 40-item online questionnaire to explore the patterns of dental service utilization. Following the descriptive statistics, the associations between A&N model factors and main outcome variables (having a dental visit in the last year and reasons for the dental visit) were evaluated using simple and multiple logistic regression analyses. RESULTS: Out of 600 potential PLHIV participants, 210 responded to the survey and 186 met the inclusion criteria. The experience of being discriminated against by dental professionals (P = 0.005), having dental anxiety (P < 0.001), not having dental insurance (P = 0.001), and having living condition difficulties (P = 0.004) were significantly associated with nonemergency dental visits. In multiple logistic regression analysis, dental anxiety (OR = 0.1; 95 percent CI 0.0; 0.4), having a regular dentist (OR = 3.7; 95 percent CI 1.1; 12.6), and visiting a dental office in the last year (OR = 21.6; 95 percent CI 6.1; 76.5) were the strongest predictors of dental service utilization in this study. CONCLUSIONS: Several predisposing, enabling, and need factors from the A&N model were associated with dental service utilization by PLHIV. In addition to various psychosocial barriers, a significant number of respondents reported experiencing stigma and discrimination from their oral care providers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".