Equity of primary care service delivery for low income “sicker” adults across 10 OECD countries
Bibliographic record
Abstract
BACKGROUND: Despite significant investments to support primary care internationally, income-based inequities in access to quality health care are present in many high-income countries. This study aims to determine whether low- and middle-income groups are more likely to report poor quality of primary care (PC) than high-income groups cross-nationally. METHODS: The 2011 Commonwealth Fund Telephone Survey of Sicker Adults is a cross-sectional study across eleven countries. Respondents were recruited from randomly selected households. We used data from surveys conducted in Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, the United Kingdom, and the United States. We identified all questions relating to primary care performance, and categorized these into five dimensions: 1) access to care, 2) coordination 3) patient-centered care, and 4) technical quality of care. We used logistic regression with low and middle-income as the comparison groups and high-income as the referent. RESULTS: Fourteen thousand two hundred sixty-two respondents provided income data. Countries varied considerably in their extent of income disparity. Overall, 24.7% were categorized as low- and 13.9% as high-income. The odds of reporting poor access to care were higher for low- and middle-income than high-income respondents in Canada, New Zealand and the US. Similar results were found for Sweden and Norway on coordination; the opposite trend favoring the low- and middle-income groups was found in New Zealand, United Kingdom, and the United States. The odds of reporting poor patient-centered care were higher for low-income than high-income respondents in the Netherlands, Norway, and the US; in Australia, this was true for low- and middle-income respondents. On technical quality of care, the odds of reporting poor care were higher for the low- and middle-income comparisons in Canada and Norway; in Germany, the odds were higher for low-income respondents only. The odds of reporting poor technical quality of care were higher for high-income than low-income respondents in the Netherlands. CONCLUSION: Inequities in quality PC for low and middle income groups exist on at least one dimension in all countries, including some that in theory provide universal access. More research is needed to fully understand equity in the PC sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".