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Record W2767222409 · doi:10.1016/s2542-5196(17)30130-4

Acquiring an operative sustainability expertise for health professionals

2017· article· en· W2767222409 on OpenAlexaff
Matthieu J. Guitton, Julien Poitras

Bibliographic record

VenueThe Lancet Planetary Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSustainabilityHealth professionalsBusinessEnvironmental planningEnvironmental resource managementNursingMedicineHealth carePolitical scienceGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

The effects of human activities on planetary ecosystems are nearing a point of no return. It is getting increasingly clear that population-wide behavioural changes will have to be the basis of any solution.1–4 Yet, in order for such changes to occur, a substantial proportion of the population has to be willing to endorse them. Because health professionals are heavily connected with all layers of the population and can have a major impact on societal issues,5–7 they might be able to encourage positive changes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0060.006
Scholarly communication0.0100.010
Open science0.0030.020
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.1060.045

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 head0.150
GPT teacher head0.467
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2017
Admission routes1
Has abstractyes

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