Constructing Taxonomies to Identify Distinctive Forms of Primary Healthcare Organizations
Bibliographic record
Abstract
Background. Primary healthcare (PHC) renewal gives rise to important challenges for policy makers, managers, and researchers in most countries. Evaluating new emerging forms of organizations is therefore of prime importance in assessing the impact of these policies. This paper presents a set of methods related to the configurational approach and an organizational taxonomy derived from our analysis. Methods. In 2005, we carried out a study on PHC in two health and social services regions of Quebec that included urban, suburban, and rural areas. An organizational survey was conducted in 473 PHC practices. We used multidimensional nonparametric statistical methods, namely, multiple correspondence and principal component analyses, and an ascending hierarchical classification method to construct a taxonomy of organizations. Results. PHC organizations were classified into five distinct models: four professional and one community. Study findings indicate that the professional integrated coordination and the community model have great potential for organizational development since they are closest to the ideal type promoted by current reforms. Conclusion. Results showed that the configurational approach is useful to assess complex phenomena such as the organization of PHC. The analysis highlights the most promising organizational models. Our study enhances our understanding of organizational change in health services organizations.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".