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Record W1605297713 · doi:10.1071/he11461

It's here! Are we ready? Five case studies of health promotion practices that address climate change from within Victorian health care settings

2011· article· en· W1605297713 on OpenAlexaboutno aff
Rebecca Patrick, Teresa Capetola

Bibliographic record

VenueHealth Promotion Journal of Australia · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersSustainability VictoriaState Government of Victoria
KeywordsHealth promotionPopulation healthPublic relationsHealth carePublic healthHealth policyMedicinePopulationEnvironmental healthNursingPolitical science

Abstract

fetched live from OpenAlex

ISSUE ADDRESSED: Climate changes and environmental degradation caused by anthropogenic activities are having an irrefutable impact on human health. The critical role played by health promotion in addressing environmental challenges has a history in seminal charters--such as the Ottawa Charter for Health Promotion--that explicitly link human well-being with the natural environment. The lack of documented practice in this field prompted an investigation of health promotion practice that addresses climate change issues within health care settings. METHODS: This qualitative study involved five case studies of Victorian health care agencies that explicitly identified climate change as a priority. Individual and group interviews with ten health promotion funded practitioners as well as document analysis techniques were used to explore diverse practices across these rural, regional and urban health care agencies. RESULTS: Health promotion practice in these agencies was oriented toward: active and sustainable transport; healthy and sustainable food supply; mental health and community resilience; engaging vulnerable population groups such as women; and organisational development. CONCLUSION: Despite differences in approach, target population and context, the core finding was that health promotion strategies, competencies and frameworks were transferable to action on climate change in these health care settings.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.526
GPT teacher head0.475
Teacher spread0.051 · 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 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

Citations26
Published2011
Admission routes1
Has abstractyes

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