Development and validation of the Japanese version of Primary Care Assessment Tool
Bibliographic record
Abstract
BACKGROUND: Tools for assessing quality of primary care from patient experience have never previously existed in Japan. OBJECTIVE: The aim of this study was to develop the Japanese version of Primary Care Assessment Tool (JPCAT) and to examine the validity of this tool in the assessment of the quality of primary care from patient experience in Japan. METHODS: We used a cross-sectional mail survey to test the validity and reliability of JPCAT. The questionnaire was sent to 1100 residents, 40-75 years of age, who were randomly selected from a basic resident register in Kita City, Tokyo, Japan. We examined internal consistency included Cronbach's alpha, exploratory factor analysis, multi-trait analysis and correlation between overall user satisfaction scores and JPCAT total scores. RESULTS: The tool was developed using responses from 204 residents, out of a total of 402 participants in the survey (50.7%), who had the usual sources of care. A 29-item JPCAT was constructed to include six multi-item subscales, representing each of the five primary care principles (first contact, longitudinality, comprehensiveness, coordination and community orientation). All of the multi-item scales achieved good internal consistency, item-total correlations and construct validity. The overall Cronbach's alpha coefficient was 0.90. The Spearman correlation coefficient between the JPCAT total score and the overall user satisfaction was 0.58. Scaling assumptions tests were well satisfied. The full range of possible scores was observed for all scales except the longitudinality domain. CONCLUSIONS: We developed JPCAT and examined its validity and reliability in assessing the core principles of primary care in Japan. This tool could be used for health service research in primary care.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".