Why Is Bigger Not Always Better in Primary Health Care Practices? The Role of Mediating Organizational Factors
Bibliographic record
Abstract
Size of primary health care (PHC) practices is often used as a proxy for various organizational characteristics related to provision of care. The objective of this article is to identify some of these organizational characteristics and to determine the extent to which they mediate the relationship between size of PHC practice and patients' experience of care, preventive services, and unmet needs. In 2010, we conducted population and organization surveys in 2 regions of the province of Quebec. We carried out multilevel linear and logistic regression analyses, adjusting for respondents' individual characteristics. Size of PHC practice was associated with organizational characteristics and resources, patients' experience of care, unmet needs, and preventive services. Overall, the larger the size of a practice, the higher the accessibility, but the lower the continuity. However, these associations faded away when organizational variables were introduced in the analysis model. This result supports the hypothesized mediating effect of organizational characteristics on relationships between practice size and patients' experience of care, preventive services, and unmet needs. Our results indicate that size does not add much information to organizational characteristics. Using size as a proxy for organizational characteristics can even be misleading because its relationships with different outcomes are highly variable.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".