Strength of primary care service delivery: a comparative study of European countries, Australia, New Zealand, and Canada
Bibliographic record
Abstract
AimWe sought to examine strength of primary care service delivery as measured by selected process indicators by general practitioners from 31 European countries plus Australia, Canada, and New Zealand. We explored the relation between strength of service delivery and healthcare expenditures. BACKGROUND: The strength of a country's primary care is determined by the degree of development of a combination of core primary care dimensions in the context of its healthcare system. This study analyses the strength of service delivery in primary care as measured through process indicators in 31 European countries plus Australia, New Zealand, and Canada. METHODS: A comparative cross-sectional study design was applied using the QUALICOPC GP database. Data on the strength of primary healthcare were collected using a standardized GP questionnaire, which included 60 questions divided into 10 dimensions related to process, structure, and outcomes. A total of 6734 general practitioners participated. Data on healthcare expenditure were obtained from World Bank statistics. We conducted a correlation analysis to analyse the relationship between strength and healthcare expenditures.FindingsOur findings show that the strength of service delivery parameters is less than optimal in some countries, and there are substantial variations among countries. Continuity and comprehensiveness of care are significantly positively related to national healthcare expenditures; however, coordination of care is not.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".