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Record W2988366291 · doi:10.3390/ijerph16224370

Heat Health Prevention Measures and Adaptation in Older Populations—A Systematic Review

2019· review· en· W2988366291 on OpenAlexaboutno aff
An V. Vu, Shannon Rutherford, Dung Phung

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsCharterSystematic reviewMedicinePsychological interventionAction (physics)PopulationMEDLINEEnvironmental healthGerontologyPsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

The population of older people is increasing at a rapid rate, with those 80 years and older set to triple by 2050. This systematic review aimed to examine older people's perceptions and behaviours against existing heatwaves prevention measures and systematically categorize and analyse those measures using the Ottawa charter for health promotion framework. Peer-reviewed published literature between 22nd September 2006 and 24th April 2018 was retrieved, according to the PRISMA guidelines, from five different databases. Eighteen articles were finally included. There is a lack of published studies from developing countries. Results were categorized and analysed using the Ottawa charter five action areas. Mitigation strategies from current heat action plans are discussed and gaps are highlighted. A lack of systematic evaluation of heat action plans efficacy was identified. Older people are not demonstrating all recommended preventative measures during heatwaves. Support personnel and health professionals are not being pro-active enough in facilitating prevention of adverse effects from heatwaves. Governments are beginning to implement policy changes, but other recommended support measures outlined in the Ottawa charter are still lacking, and hence require further action. Linkage between specific components of heat action plans and outcomes cannot be ascertained; therefore, more systematic evaluation is needed.

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)0.008
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.810
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.486
GPT teacher head0.516
Teacher spread0.030 · 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 designSystematic review
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

Citations58
Published2019
Admission routes1
Has abstractyes

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