Prospective cohort study protocol to evaluate the validity and reliability of the Quality of Trauma Care Patient-Reported Experience Measure (QTAC-PREM)
Bibliographic record
Abstract
BACKGROUND: Patient-centeredness is a key component of health care quality. However, patient-centered measures of quality have not been developed in injury care. In response to this challenge, we developed the Quality of Trauma Adult Care Patient-Reported Experience Measure (QTAC-PREM) to measure injured patient experiences with trauma care and pilot-tested the instrument at a single Level 1 trauma centre. The objective of this study is to test the reliability, validity, and feasibility of the QTAC-PREM in multiple Canadian trauma centers and to refine the measure based on the results. METHODS/DESIGN: This will be a prospective cohort study of consecutive adult (age ≥ 18 years) patients discharged from three trauma centres in Alberta, Canada with a primary diagnosis of injury. The target sample size is 400 participants to ensure precision for evaluating test-retest reliability. We will assess the psychometric properties of the measure (test-retest reliability, construct validity, internal consistency) and whether these properties vary by patient characteristics. We will also evaluate the predictive validity, convergent validity, and discriminant validity of the measure against other established tools (HCAHPS). DISCUSSION: A reliable and valid measure of patient reported experiences with injury care may be a valuable tool to evaluate quality of care and guide improvement efforts.
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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 | 0.042 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".