Factors associated with multiple barriers to access to primary care: an international analysis
Bibliographic record
Abstract
BACKGROUND: Disparities in access to primary care (PC) have been demonstrated within and between health systems. However, few studies have assessed the factors associated with multiple barriers to access occurring along the care-seeking process in different healthcare systems. METHODS: In this secondary analysis of the 2016 Commonwealth Fund International Health Policy Survey of Adults, access was represented through participant responses to questions relating to access barriers either before or after reaching the PC practice in 11 countries (Australia, Canada, France, Germany, Norway, the Netherlands, New Zealand, Sweden, Switzerland, the United Kingdom, and United States). The number of respondents in each country ranged from 1000 to 7000 and the response rates ranged from 11% to 47%. We used multivariable logistic regression models within each of eleven countries to identify disparities in response to the access barriers by age, sex, immigrant status, income and the presence of chronic conditions. RESULTS: Overall, one in five adults (21%) experienced multiple barriers before reaching PC practices. After reaching care, an average of 16% of adults had two or more barriers. There was a sixfold difference between nations in the experience of these barriers to access. Vulnerable groups experiencing multiple barriers were relatively consistent across countries. People with lower income were more likely to experience multiple barriers, particularly before reaching primary care practices. Respondents with mental health problems and those born outside the country displayed substantial vulnerability in terms of barriers after reaching care. CONCLUSION: A greater understanding of the multiple barriers to access to PC across the stages of the care-seeking process may help to inform planning and performance monitoring of disparities in access. Variation across countries may reveal organisational and system drivers of access, and inform efforts to improve access to PC for vulnerable groups. The cumulative nature of these barriers remains to be assessed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".