Quality indicators for cardiac care: national standards in a community context
Bibliographic record
Abstract
OBJECTIVE: Public reporting of health data is well established in the United States and in the United Kingdom, and is assumed to promote better health care through informed choice by consumers. To be successful, reporting systems must have the support of physicians, but their opinions have been mixed. The purpose of this study was to explore with practising physicians the perceived usefulness of, and barriers to use of, quality indicators in the care of acute myocardial infarction and congestive heart failure, and the contexts in which these issues arise. METHODS: Six focus groups were conducted in small-, medium- and large-sized communities in two provinces in Canada. Subjects were family physicians, emergency physicians, internists and cardiologists. Data were analysed inductively. RESULTS: Our participants were generally supportive of the quality indicators, with concerns expressed regarding interpretation of data from measures created by "experts" but applied in the context of community hospitals and community-based practice. Content analysis disclosed that a majority of the indicators was acceptable; few were outright unacceptable. Inductive analysis revealed two contextual concerns: issues arising from the structure and organization of the health care system, such as equitable access to health care resources and discontinuity or fragmentation of the system, and patient-related issues, such as compliance with medications post-discharge and costs of medications. CONCLUSIONS: There is general support for this set of quality indicators, with the caveat that data should be carefully interpreted in the context of each community in which they are applied.
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 | 0.043 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".