Development and Validation of the Modified Patient‐Centered Medical Home Assessment for the Comprehensive Primary Care Initiative
Bibliographic record
Abstract
OBJECTIVE: To describe the modified Patient-Centered Medical Home Assessment (M-PCMH-A) survey module developed to track primary care practices' care delivery approaches over time, assess whether its underlying factor structure is reliable, and produce factor scores that provide a more reliable summary measure of the practice's care delivery than would a simple average of question responses. DATA SOURCES/STUDY SETTING: Survey data collected from diverse practices participating in the Comprehensive Primary Care (CPC) initiative in 2012 (n = 497) and 2014 (n = 493) and matched comparison practices in 2014 (n = 423). STUDY DESIGN: Confirmatory factor analysis. DATA COLLECTION: Thirty-eight questions organized in six domains: Access and Continuity of Care, Planned Care for Chronic Conditions and Preventive Care, Risk-Stratified Care Management, Patient and Caregiver Engagement, Coordination of Care across the Medical Neighborhood, and Continuous Data-Driven Improvement. PRINCIPAL FINDINGS: Confirmatory factor analysis suggested using seven factors (splitting one domain into two), reassigning two questions to different domain factors, and removing one question, resulting in high reliability, construct validity, and stability in all but one factor. The seven factors together formed a single higher-order factor summary measure. Factor scores guard against potential biases from equal weighting. CONCLUSIONS: The M-PCMH-A can validly and reliably track primary care delivery across practices and over time using factors representing seven key components of care as well as an overall score. Researchers should calculate factor loadings for their specific data if possible, but average scores may be suitable if they cannot use factor analysis due to resource or sample constraints.
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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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".