MétaCan
Menu
Back to cohort
Record W4200590462 · doi:10.25071/2291-5796.105

Nurses as Boundary Actors in Sustainable Health Care: A Discussion Paper

2021· article· en· W4200590462 on OpenAlex
Joanna Law, Maya R. Kalogirou, Sherry Dahlke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsHealth careClimate changeWork (physics)SustainabilityBusinessAction (physics)Call to actionNursingEnvironmental resource managementPublic relationsMedicinePsychologyEnvironmental planningPolitical scienceEconomic growthGeographyEconomicsMarketing

Abstract

fetched live from OpenAlex

The devastating global health impacts of climate change are becoming more apparent and more frequent. Health care systems are increasingly burdened by the response to these impacts. Paradoxically, as they respond to the negative health effects of climate change, these same resource intense health care systems are contributing to further climate change. Organizations and academics have issued a call to action for health care workers to mitigate climate change and promote environmental sustainability. Nurses are an integral part of health care systems but have been delayed in answering this call. In this paper we argue that nurses are particularly well suited to mitigating climate change in health care systems because their existing role is central to patient care, and as a result they interface with other health care providers and have developed proficiency in articulation work.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.528
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.357
Teacher spread0.338 · 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