Patient-reported confidence in primary healthcare: are there disparities by ethnicity or language?
Bibliographic record
Abstract
OBJECTIVE: To examine whether confidence in primary healthcare (PHC) differs among ethnic-linguistic groups and which PHC experiences are associated with confidence. DESIGN: A cross-sectional study where patient surveys were administered using random digit dialling. Regression models identify whether ethnic-linguistic group remains significantly associated with confidence in PHC. SETTING: British Columbia, Canada. MAIN OUTCOME MEASURES: Confidence in PHC measured using a 0-10 scale, where a higher score indicates increased confidence in the ability to get needed PHC services. PARTICIPANTS: Community-dwelling adults in the following ethnic-linguistic groups: English-speaking Chinese, Chinese-speaking Chinese, English-speaking South Asians, Punjabi-speaking South Asians and English-speakers of presumed European descent. FINDINGS: Based on a sample of 1211 respondents, confidence in PHC differed by ethnicity and the ability to speak English. Most of the differences in confidence by ethnic-linguistic group can be explained by various aspects of care experience. Patient experiences associated with lower confidence in PHC were: if care was received outside Canada, having to wait months to see their regular doctor and rating the quality of healthcare as good or fair/poor. Better patient experiences of their doctor being concerned about their feelings and being respectful and if they found wait times acceptable were associated with higher levels of confidence in PHC. The final regression model explained 30% of the variance. CONCLUSIONS: Improving the delivery of PHC services through positive interactions between patients and their usual provider and acceptability of wait times are examples of how the PHC system can be strengthened.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".