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Record W3175335583 · doi:10.25071/2291-5796.66

The politics of public health: A rapid review of the impact of public health reform on population health outcomes

2021· review· en· W3175335583 on OpenAlexaffvenueabout
Charlotte Riordon, Sionnach Hendra, Christine P. Johnson

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2021
Typereview
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsEquity (law)PoliticsRestructuringHealth equityPublic healthPolitical scienceSocial determinants of healthHealth careHealth policyPublic economicsPopulation healthPosition (finance)PopulationEconomic growthPublic relationsBusinessPublic administrationEconomicsMedicineEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Canada’s public health (PH) systems are vulnerable to constant system and structural changes, influenced by political and economic factors. This rapid review examines how PH system restructuring impacts population health outcomes, with special consideration of health equity. Due to a lack of Canadian evidence, international research was examined to produce recommendations for Canadian nurses, researchers, and decision-makers. Evidence indicates that PH spending and PH system organization have important impacts on population health outcomes and suggests PH reform has a negative impact on health equity. Opportunities for advocacy, activism, lobbying and capacity building to achieve health equity are discussed. Nurses, in a unique position between public policy and the lives of those they care for, are presented with the opportunity to effect social change through political action and to work across disciplines to address inequities. We encourage researchers and decision-makers to prioritize looking more deeply at the impact of PH reform.

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)0.020
metaresearch head score (Gemma)0.009
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.279
GPT teacher head0.577
Teacher spread0.298 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes3
Has abstractyes

Explore more

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