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Record W2150515727 · doi:10.1177/1527154411429198

Public Policy Analysis to Redress Urban Environmental Health Inequities

2011· article· en· W2150515727 on OpenAlexafffundabout
Andrea Chircop

Bibliographic record

VenuePolicy Politics & Nursing Practice · 2011
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsDalhousie University
FundersHealth CanadaKillam TrustsDalhousie University
KeywordsRedressPublic policyPublic healthHealth policyPolicy analysisPolitical scienceContext (archaeology)PovertyEconomic growthSocial policySociologyHealth carePublic administrationGeographyEconomicsMedicine

Abstract

fetched live from OpenAlex

Public policies may not have been designed to disadvantage certain populations, but the effects of some policies create unintended health inequities. The nature of community health nurses' daily work provides a privileged position to witness the lived experiences and effects of policy-produced social and health inequities. This privileged position requires policy competence including analytical skills to connect lived experiences to public policy. The purpose of this article is to present an example of an urban ethnography that explicates inequity-producing effects of public policy and is intended to inform necessary policy changes. This study sheds light on how issues of childcare, housing, nutrition, and urban infrastructure in the context of poverty are fundamental to the larger issues of environmental health. This policy analysis documents how the Day Care Act of Nova Scotia, Canada explicates patriarchal and neoliberal gender and class assumptions that have implications for mothers' health decisions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.218
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.007
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.390
GPT teacher head0.523
Teacher spread0.133 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2011
Admission routes3
Has abstractyes

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