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Record W3120181426 · doi:10.1108/jhom-09-2020-0370

Divergent notions of “quality” in healthcare policy implementation: a framing perspective

2021· article· en· W3120181426 on OpenAlexaffabout
Husayn Marani, Jenna M. Evans, Karen S. Palmer, Adalsteinn Brown, Danielle Martin, Noah Ivers

Bibliographic record

VenueJournal of Health Organization and Management · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsPublic Health OntarioSt. Michael's HospitalSimon Fraser UniversityMcMaster UniversityWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)Quality policyPublic relationsHealth careCorporate governancePolicy analysisQualitative researchHealth policyFrame analysisQuality managementSociologyPublic administrationPolitical scienceBusinessContent analysisMarketingEngineeringFinanceSocial science

Abstract

fetched live from OpenAlex

PURPOSE: This paper examines how "quality" was framed in the design and implementation of a policy to reform hospital funding and associated care delivery. The aims of the study were: (1) To describe how government policy-makers who designed the policy and managers and clinicians who implemented the policy framed the concept of "quality" and (2) To explore how frames of quality and the framing process may have influenced policy implementation. DESIGN/METHODOLOGY/APPROACH: The authors conducted a secondary analysis of data from a qualitative case study involving semi-structured interviews with 45 purposefully selected key informants involved in the design and implementation of the quality-based procedures policy in Ontario, Canada. The authors used framing theory to inform coding and analysis. FINDINGS: The authors found that policy designers perpetuated a broader frame of quality than implementers who held more narrow frames of quality. Frame divergence was further characterized by how informants framed the relationship between clinical and financial domains of quality. Several environmental and organizational factors influenced how quality was framed by implementers. ORIGINALITY/VALUE: As health systems around the world increasingly implement new models of governance and financing to strengthen quality of care, there is a need to consider how "quality" is framed in the context of these policies and with what effect. This is the first framing analysis of "quality" in health policy.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.288
GPT teacher head0.648
Teacher spread0.360 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2021
Admission routes2
Has abstractyes

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