Psychometric analysis of the TRANSIT quality indicators for cardiovascular disease prevention in primary care
Bibliographic record
Abstract
OBJECTIVE: To assess a selection of psychometric properties of the TRANSIT indicators. DESIGN: Using medical records, indicators were documented retrospectively during the 14 months preceding the end of the TRANSIT study. SETTING: Primary care in Quebec, Canada. PARTICIPANTS: Indicators were documented in a random subsample (n = 123 patients) of the TRANSIT study population (n = 759). INTERVENTIONS: For every patient, the mean compliance to all indicators of a category (subscale score) and to the complete set of indicators (overall scale score) were established. To evaluate test-retest and inter-rater reliabilities, indicators were applied twice, two months apart, by the same evaluator and independently by different evaluators, respectively. To evaluate convergent validity, correlations between TRANSIT indicators, Burge et al. indicators and Institut national d'excellence en santé et en services sociaux (INESSS) indicators were examined. MAIN OUTCOME MEASURES: Test-retest reliability, inter-rater reliability, and convergent validity. RESULTS: Test-retest reliability, as measured by intraclass correlation coefficients (ICCs) was equal to 0.99 (0.99-0.99) for the overall scale score while inter-rater reliability was equal to 0.95 (0.93-0.97) for the overall scale score. Convergent validity, as measured by Pearson's correlation coefficients, was equal to 0.77 (P < 0.001) for the overall scale score when the TRANSIT indicators were compared to Burge et al. indicators and to 0.82 (P < 0.001) for the overall scale score when the TRANSIT indicators were compared to INESSS indicators. CONCLUSIONS: Reliability was excellent except for eleven indicators while convergent validity was strong except for domains related to the management of CVD risk factors.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".