Patients' views about cardiac report cards: a qualitative study.
Bibliographic record
Abstract
BACKGROUND: Health care report cards provide stakeholders with information on health care outcomes and other measures of care, and they are most well developed in cardiac care. A necessary first step to ground the development of cardiac report cards (CRCs) is to incorporate the views of stakeholders. Although the views of experts have been described, the views of cardiac patients, arguably the most important stakeholders, have not yet been described. OBJECTIVE: To describe cardiac patients' views about CRCs. METHODS: Qualitative interviews were conducted with 91 cardiac patients contacted from seven Canadian cardiac care centres. Participants' views regarding CRCs were analyzed and organized into themes. RESULTS: Participants' views were organized into four themes: overall views, purpose, content and dissemination. Participants expressed overwhelmingly positive views about CRCs and thought that CRCs should be used to improve the quality of cardiac care, enhance accountability and improve informed decision-making. They said that they would use CRCs that contained information relevant to patients -- in particular, information about other cardiac patients' experiences. They described a patient-derived framework for the content of CRCs. Participants also described dissemination formats and vehicles that would increase the usefulness of CRCs. CONCLUSIONS: The cardiac patients in the present study had positive attitudes about CRCs and would use them if they were designed to be relevant to patients. In particular, the participants wanted CRCs to provide information about other cardiac patients' experiences.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".