Framing the Problem of Measuring and Improving Healthcare Quality
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the uses of the Quality Health Outcomes framework and indicator categories in the healthcare literature. DATA SOURCES: We studied personal communications and conducted a literature search using computerized databases since 1997, when the recommendations of the Invitational Conference on Measures and Outcomes of Care Delivery were available. PRINCIPAL FINDINGS: The Quality Health Outcomes Model has been used explicitly to frame a small number of research summaries and programs. The outcome indicator categories can be found in several "report card" initiatives in the United States and Canada. Use of these outcome categories, thought to be sensitive to nursing care inputs, has grown since 1977, with a rising number of uses linked to system or organizational factors or interventions. CONCLUSIONS: This model and others like it are increasingly forming the conceptual framework for studies that evaluate quality and system interventions to improve care. However, the available data continue to require the linking of negative outcomes (adverse events, complications) to structural and process inputs that reflect nursing care. An urgent need remains to incorporate this broader range of outcomes into available databases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.267 | 0.405 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.023 | 0.051 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.013 | 0.014 |
| 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; 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".