Development of primary care assessment tool–adult version in Tibet: implication for low- and middle-income countries
Bibliographic record
Abstract
AIM: To conduct advanced psychometric analysis of Primary Care Assessment Tool (PCAT) in Tibet and identify avenues for metric performance improvement. BACKGROUND: Measuring progress toward high-performing primary health care can contribute to the achievement of sustainable development goals. The adult version of PCAT is an instrument for measuring patient experience, with key elements of primary care. It has been extensively used and validated internationally. However, only little information is available regarding its psychometric properties obtained based on advanced analysis. METHODS: We used data collected from 1386 primary care users in two prefectures in Tibet. First, iterative confirmatory factor analysis examined the fit of the primary care construct in the original tool. Then item response theory analysis evaluated how well the questions and individual response options perform at different levels of patient experience. Finally, multiple logistic regression modeling examined the predicative validity of primary care domains against patient satisfaction. FINDINGS: A best final structure for the PCAT-Tibetan includes 7 domains and 27 items. Confirmatory factor analysis suggests good fit for a unidimensional model for items within each domain but doesn't support a unidimensional model for the entire instrument with all domains. Non-parametric and parametric item response theory analysis models show that for most items, the favorable response option (4 = definitely) is overwhelmingly endorsed, the discriminability parameter is over 1, and the difficulty parameters are all negative, suggesting that the items are most sensitive and specific for patients with poor primary care experience. Ongoing care is the strongest predictor of patient satisfaction. These findings suggest the need for some principles in adapting the tool to different health system contexts, more items measuring excellent primary care experience, and update of the four-point response options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".