Identification, summary and comparison of tools used to measure organizational attributes associated with chronic disease management within primary care settings
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Given the increasing emphasis being placed on managing patients with chronic diseases within primary care, there is a need to better understand which primary care organizational attributes affect the quality of care that patients with chronic diseases receive. This study aimed to identify, summarize and compare data collection tools that describe and measure organizational attributes used within the primary care setting worldwide. METHODS: Systematic search and review methodology consisting of a comprehensive and exhaustive search that is based on a broad question to identify the best available evidence was employed. RESULTS: A total of 30 organizational attribute data collection tools that have been used within the primary care setting were identified. The tools varied with respect to overall focus and level of organizational detail captured, theoretical foundations, administration and completion methods, types of questions asked, and the extent to which psychometric property testing had been performed. The tools utilized within the Quality and Costs of Primary Care in Europe study and the Canadian Primary Health Care Practice-Based Surveys were the most recently developed tools. Furthermore, of the 30 tools reviewed, the Canadian Primary Health Care Practice-Based Surveys collected the most information on organizational attributes. CONCLUSIONS: There is a need to collect primary care organizational attribute information at a national level to better understand factors affecting the quality of chronic disease prevention and management across a given country. The data collection tools identified in this review can be used to establish data collection strategies to collect this important information.
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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 | 0.120 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".