Getting it all done. Organizational factors linked with comprehensive primary care
Bibliographic record
Abstract
BACKGROUND: Comprehensiveness, a defining feature of primary care (PC) is associated with patient satisfaction and improved health status. This paper evaluates comprehensive services in fee-for-service (FFS), Health Service Organizations (HSOs), Family Health Networks (FHNs) and Community Health Centres (CHCs) payment models in Ontario. OBJECTIVES: To assess how organizational models of PC differ in the delivery of comprehensive services and which organizational factors predict comprehensive PC delivery. METHODS: Cross-sectional mixed-method study with nested qualitative case studies. SETTING: PC practices in Ontario. PARTICIPANTS: One hundred and thirty-seven PC practices (35 FFS, 32 HSO, 35 FHN and 35 CHC) and 358 providers. INSTRUMENTS: Surveys based on the Primary Care Assessment Tool and qualitative interviews. OUTCOME MEASURES: Comprehensiveness scores were calculated from practice report of clinical services offered in women's health, psychosocial counselling, procedural and diagnostic services. Confounding variables were calculated from provider and patient surveys. Performance at a model level was compared using analysis of variance. Multiple regressions then established factors independently associated with comprehensiveness. RESULTS: CHCs offered significantly more comprehensive services (74%) than other models (61%-63%; P < 0.005). Thirty-five per cent of the variance in comprehensiveness was explained by a regression model that included the number of family physicians working at the practice, presence of other allied health providers, rurality and length of practice operation. CONCLUSIONS: Practice size and diversity of providers seemed to partially explain the better performance of CHCs. Practice setting and, probably, practice maturity are significant drivers in the provision of comprehensive PC services. These factors warrant further examination in other PC environments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".