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Record W4319301877 · doi:10.1016/s2542-5196(22)00311-4

A qualitative study of what motivates and enables climate-engaged physicians in Canada to engage in health-care sustainability, advocacy, and action

2023· review· en· W4319301877 on OpenAlexaffabout
Owen Dan Luo, Yasmeen Razvi, Gurleen Kaur, Michelle H. Lim, Kelti Smith, Jacob Joel Kirsh Carson, Claudel Petrin-Desrosiers, Victoria Haldane, Nicole Simms, Fiona A. Miller

Bibliographic record

VenueThe Lancet Planetary Health · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsInstitute for Work & HealthQueen's UniversityUniversity of TorontoUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsSustainabilityHealth careNursingPublic relationsQualitative researchHealth policyBusinessPsychologyMedicinePolitical sciencePublic healthSociology

Abstract

fetched live from OpenAlex

Increasing numbers of health-care professionals are aware of the need to deliver low-carbon sustainable health systems. We aimed to explore how physicians can be motivated and supported to pursue this ambition by conducting an exploratory qualitative descriptive study that involved individual in-depth interviews with climate-engaged Canadian physicians participating in health-care sustainability advocacy and action. Interview transcripts were analysed to identify themes related to the actions that physicians can take to promote sustainable health care, and the motivators and enablers of physician engagement in sustainable health care. Participants (n=19) engaged in a spectrum of health-care sustainability initiatives ranging from reducing health-care waste to lobbying and political action. They were motivated to advance health-care sustainability by their concern about the health implications of climate change, frustration with health-care waste, and recognition of their locus of influence as physicians. Participants articulated that policy and system, organisational and team, and knowledge generation and translation supports are required to strengthen their capacity to advance health-care sustainability. These findings can provide inspiration for engagement opportunities in health-care sustainability, guide service delivery and educational innovations to promote health-care professionals' interest in becoming sustainability champions, and extend the capacity of health-care professionals to reduce the climate impact of health care.

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)0.005
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.183
GPT teacher head0.439
Teacher spread0.256 · 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
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

Citations47
Published2023
Admission routes2
Has abstractyes

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