Multicenter validation of the Quality of Trauma Care Patient-Reported Experience Measure (QTAC-PREM)
Bibliographic record
Abstract
BACKGROUND: Incorporating patient and family perspectives into injury care quality assessment is a necessary part of comprehensive quality improvement. However, tools to measure patient and family perspectives of injury care are lacking. Therefore, our objective was to assess the psychometric properties of the Quality of Trauma Care Patient-Reported Experience Measure (QTAC-PREM), the first measure developed to assess patient experiences with overall injury care. METHODS: We conducted a prospective multicenter cohort study of adult injury patients recruited from three trauma centers. Patients or surrogates completed an acute care survey measure in the hospital and a post-acute care survey measure after hospital discharge. RESULTS: Four hundred participants (78%) completed the acute care measure, and 207 (59%) completed the post-acute care measure. We identified three subscales on the acute measure and two subscales on the post-acute measure. All subscales and items had evidence of construct validity. Four subscales had good internal consistency, and three were independent predictors of participants' overall ratings of injury care quality. The majority of items demonstrated suitable test-retest reliability. Comparison of QTAC-PREM scores with those of an existing patient experience tool, the Hospital version of the Consumer Assessment of Healthcare Providers and Systems (HCAHPS), demonstrated evidence of appropriate divergent and convergent validity. CONCLUSION: This study demonstrates that the QTAC-PREM is feasible to implement at trauma centers and provides evidence of validity and reliability. The tool may be useful to incorporate patient perspectives into trauma care quality measurement and improvement.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".