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Record W2593940776 · doi:10.12927/hcpol.2017.25028

Assessing Continuous Quality Improvement in Public Health: Adapting Lessons from Healthcare

2017· article· en· W2593940776 on OpenAlexaffvenueabout
Alex Price, Robert Schwartz, Joanna E Cohen, Heather Manson, Fran Scott

Bibliographic record

VenueHealthcare policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsHamilton Health SciencesMcMaster UniversityThe Canadian Association of Professional Academic LibrariansPublic Health Ontario
Fundersnot available
KeywordsQuality managementAccountabilityHealth careQuality (philosophy)Public healthTotal quality managementBusinessProcess managementPublic healthcareMedicineNursingPolitical scienceMarketing

Abstract

fetched live from OpenAlex

CONTEXT: Evidence of the effect of continuous quality improvement (CQI) in public health and valid tools to judge that such effects are not fully formed. OBJECTIVE: The objective was to adapt and apply Shortell et al.'s (1998) four dimensions of CQI in an examination of a public health accountability and performance management initiative in Ontario, Canada. METHODS: In total, 24 semi-structured, in-depth interviews were conducted with informants from public health units and the Ministry of Health and Long-Term Care. A web survey of public health managers in the province was also carried out. RESULTS: A mix of facilitators and barriers was identified. Leadership and organizational cultures, conducive to CQI success were evident. However, limitations in performance measurement and managerial discretion were key barriers. CONCLUSION: The four dimensions of CQI provided insight into both facilitators and barriers of CQI adoption in public health. Future research should compare the outcomes of public health CQI initiatives to the framework's stated facilitators and barriers.

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)0.015
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
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 head0.852
GPT teacher head0.738
Teacher spread0.113 · 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.

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

Citations9
Published2017
Admission routes3
Has abstractyes

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