Measuring quality of care: considering measurement frameworks and needs assessment to guide quality indicator development
Bibliographic record
Abstract
OBJECTIVE: In this article, we describe one approach for evaluating the value of developing quality indicators (QIs). STUDY DESIGN AND SETTING: We focus on describing how to develop a conceptual measurement framework and how to evaluate the need to develop QIs. A recent process to develop QIs for injury care is used for illustration. RESULTS: Key steps to perform before developing QIs include creating a conceptual measurement framework, determining stakeholder perspectives, and performing a QI needs assessment. QI development is likely to be most beneficial for medical problems for which quality measures have not been previously developed or are inadequate and that have a large burden of illness to justify quality measurement and improvement efforts, are characterized by variable or substandard care such that opportunities for improvement exist, and have evidence that improving quality of care will improve patient health. CONCLUSION: By developing a conceptual measurement framework and performing a QI needs assessment, developers and users of QIs can target their 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 | 0.383 | 0.491 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".