Population experiences of primary care in 11 Organization for Economic Cooperation and Development countries
Bibliographic record
Abstract
OBJECTIVE: To develop a measure of individual user assessments of primary care and test its association with health system performance and quality indicators. DESIGN: Cross-sectional analysis of secondary survey data collected in 2013. SETTING: Australia, Canada, France, Germany, Netherlands, New Zealand, Norway, Sweden, Switzerland, the UK and the USA. STUDY PARTICIPANTS: 20 045 respondents. MAIN OUTCOME MEASURES: Individual report of financial protection (out of pocket expenses over USD 1000), lack of receipt of appropriate/timely care (use of the emergency room in the past 2 years, having consulted three of more doctors in the past year) and clinical prevention (blood pressure check in past year, cholesterol checked in the past 5 years, receipt of influenza vaccination in past year and report of any medical error). METHODS: A score of users' primary care experiences was constructed from 14 individual survey questions. Multivariable Poisson and augmented inverse-probability weighted regression assess the relationship between the primary care experience score and outcomes. RESULTS: Countries differed regarding the proportion of the population experiencing problems with primary care. In analyses controlling for age, sex, health status, chronic disease, income level and health insurance, users experiencing poorer primary care were significantly more likely to report significant out of pocket expenses, emergency room use in the past 2 years, having consulted more than three doctors in the past year, lower likelihood of blood pressure or cholesterol screening, an annual flu shot and higher reports of medical error. CONCLUSIONS: The measure of individual primary care experience can be used to differentiate among different country's primary care approaches and is strongly associated with overall health system performance and quality indicators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".