Developing a patient and family-centred approach for measuring the quality of injury care: a study protocol
Bibliographic record
Abstract
BACKGROUND: Quality indicators (QI) are used in health care to measure quality of service and performance improvement. Health care professionals and organizations caring for patients with injuries need information regarding the quality of care provided and the outcomes experienced in order to target improvement efforts. However, very little is known about the quality of injury care provided to individual patients and populations and even less about patients' perspectives on quality of care. The absence of QIs that incorporate patient or family preferences, needs or values has been identified as an important gap in the science and practice of injury quality improvement. The primary objective of this research protocol is to develop and evaluate the first set of patient and family-centred QIs of injury care for critically injured patients METHODS/DESIGN: This mixed methods study is comprised of three Sub-Studies. Sub-Study A will utilize focus group methodology to describe the preferences, needs and values of critically injured patients and their family members regarding the quality of health care delivered. Qualitative content analysis of the transcripts will begin after the first completed focus group and will draw on grounded theory using a process of open, axial and selective coding. A panel of stakeholders will be assembled during Sub-Study B to review the themes identified from the focus groups and develop a catalogue of potential patient and family-centred QIs of injury care using the RAND/UCLA Appropriateness Method (RAM). The QIs developed by the stakeholder panel will be pilot tested in Sub-Study C using surveys of patients and their family members to determine construct validity, intra-rater reliability and clinical sensibility. DISCUSSION: Measuring the quality of injury care is but a first step towards improving patient outcomes. This research will develop the first set of patient and family-centred QIs of injury care. To improve patient care, we need accessible, reliable indicators of quality that are important to patients, and that can then be used to establish quality of care benchmarks, to flag potential problems or successes, follow trends over time and identify disparities across organizations, communities, populations and regions.
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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 | 0.097 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".