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Record W2002466763 · doi:10.1080/13549839.2013.812625

Building bridges between health promotion and social sustainability: an analysis of municipal policies in Western Canada

2013· article· en· W2002466763 on OpenAlexaffabout
Lori Baugh Littlejohns, Neale Smith

Bibliographic record

VenueLocal Environment · 2013
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal HealthUniversity of Alberta
Fundersnot available
KeywordsHealth promotionInclusion (mineral)General partnershipSustainabilityPromotion (chess)Social determinants of healthPublic relationsPopulation healthPublic healthHealth policyPolitical sciencePopulationEconomic growthSociologyEnvironmental healthMedicineNursingSocial scienceEconomics

Abstract

fetched live from OpenAlex

This paper reports an analysis of municipal policies through a population health-promotion lens; its purpose is to identify potential grounds for intersectoral collaboration. Template analysis methods based on widely accepted determinants of health (DOH) and health-promotion strategies were used to study policies from six Western Canadian municipalities: Vancouver, Victoria, Surrey, Edmonton, Calgary and Strathcona County. To illustrate overlapping concepts, findings regarding rich descriptions of social environments (one determinant of health) are highlighted. Similarly, social inclusion as a desired goal and community capacity building (as both process and outcome), appear to be concepts around which health promotion and social sustainability frames converge. These findings identify potentially fertile partnership opportunities: health promoters can deepen their understanding of DOH such as social inclusion and municipal leaders can learn from the evidence of effectiveness in health-promotion strategies.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.012
Science and technology studies0.0110.003
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.385
Teacher spread0.336 · 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 designQualitative
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

Citations4
Published2013
Admission routes2
Has abstractyes

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