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Record W2103090476 · doi:10.1093/heapro/dar055

Health promotion and climate change: exploring the core competencies required for action

2011· article· en· W2103090476 on OpenAlexaboutno aff
Rebecca Patrick, Teresa Capetola, M. A. Townsend, Sonia Nuttman

Bibliographic record

VenueHealth Promotion International · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersDeakin University
KeywordsHealth promotionCharterSocial determinants of healthPolitical scienceContext (archaeology)Public relationsHealth policyClimate changeSustainabilityPublic healthEconomic growthMedicineGeographyNursingEcology

Abstract

fetched live from OpenAlex

Climate change poses serious threats to human health and well-being. It exacerbates existing health inequities, impacts on the social determinants of health and disproportionately affects vulnerable populations. In the Australian region these include remote Aboriginal communities, Pacific Island countries and people with low incomes. Given health promotion's remit to protect and promote health, it should be well placed to respond to emerging climate-related health challenges. Yet, to date, there has been little evidence to demonstrate this. This paper draws on the findings of a qualitative study conducted in Victoria, Australia to highlight that; while there is clearly a role for health promotion in climate change mitigation and adaptation at the national and international levels, there is also a need for the engagement of health promoters at the community level. This raises several key issues for health promotion practice. To be better prepared to respond to climate change, health promotion practitioners first need to re-engage with the central tenets of the Ottawa Charter, namely the interconnectedness of humans and the natural environment and, secondly, the need to adopt ideas and frameworks from the sustainability field. The findings also open up a discussion for paradigmatic shifts in health promotion thinking and acting in the context of climate change.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.573
GPT teacher head0.426
Teacher spread0.147 · 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.

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

Citations51
Published2011
Admission routes1
Has abstractyes

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