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Record W2130031383 · doi:10.1186/1472-6939-6-3

An ethical framework for cardiac report cards: a qualitative study

2005· article· en· W2130031383 on OpenAlexafffund
Shawn A. Richard, Shail Rawal, Douglas K. Martin

Bibliographic record

VenueBMC Medical Ethics · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareHeart and Stroke Foundation of Canada
KeywordsPhilosophy of medicineStakeholderTransparency (behavior)AccountabilityEngineering ethicsDelphi methodHealth careContext (archaeology)Public relationsKnowledge managementMedicinePsychologyPolitical scienceComputer scienceEngineeringAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The recent proliferation of health care report cards, especially in cardiac care, has occurred in the absence of an ethical framework to guide in their development and implementation. An ethical framework is a consistent and comprehensive theoretical foundation in ethics, and is formed by integrating ethical theories, relevant literature, and other critical information (such as the views of stakeholders). An ethical framework in the context of cardiac care provides guidance for developing cardiac report cards (CRCs) that are relevant and legitimate to all stakeholders. The purpose of this study is to develop an ethical framework for CRCs. METHODS: Delphi technique - 13 panelists: 2 administrators, 2 cardiac nurses, 5 cardiac patients, 2 cardiologists, 1 member of the media, and 1 outcomes researcher. Panelists' views regarding the ethics of CRCs were analyzed and organized into themes. RESULTS: We have organized panelists' views into ten principles that emerged from the data: 1) improving quality of care, 2) informed understanding, 3) public accountability, 4) transparency, 5) equity, 6) access to information 7) quality of information, 8) multi-stakeholder collaboration, 9) legitimacy, and 10) evaluation and continuous quality improvement. CONCLUSION: We have developed a framework to guide the development and dissemination of CRCs. This ethical framework can provide necessary guidance for those generating CRCs and may help them avoid a number of difficult issues associated with existing ones.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.244
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.244
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.332
GPT teacher head0.641
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2005
Admission routes2
Has abstractyes

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