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Record W4386562417 · doi:10.12927/hcpol.2023.27154

How Timid or Bold Are Ministries of Health and Provincial Health Authorities in Canada in Planning for Healthcare Quality?

2023· article· en· W4386562417 on OpenAlexaffvenueabout
Benjamin T.B. Chan, Susmitha Rallabandi, Dan Florizone

Bibliographic record

VenueHealthcare policy · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of SaskatchewanNOSM University
Fundersnot available
KeywordsRubricQuality (philosophy)Health careSet (abstract data type)CriticismPolitical sciencePublic administrationPublic relationsBusinessPsychologyComputer scienceLaw

Abstract

fetched live from OpenAlex

Introduction: World Health Organization (WHO) guidelines recommend countries set quality plans for their health systems with clear priorities, indicators and targets. This paper examines whether Canada's federal, provincial and territorial governments are applying these principles. Methods: We evaluated plans from 2010 to 2019 for 14 ministries of health and four health authorities in provinces with a single authority against a rubric that considered the existence of indicators, baselines, targets, time frames and progress reports. Results: Ratings ranged from A+ to F with a median B/B-. Most jurisdictions had indicators, but only five of 18 jurisdictions had clear baselines, numeric targets and time frames. Irregularities were observed, such as vague indicators; setting goals to "improve" without targets; announcing targets only after plans had ended; setting minimal targets; removing targets after missing them previously; or inappropriate characterization of progress. Discussion: Most Canadian governments are reluctant to set quality targets. We speculate there may be fear of criticism if targets are missed. However, several jurisdictions had clear, ambitious plans that may serve as examples for others.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.114
GPT teacher head0.418
Teacher spread0.304 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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