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Record W3049124717 · doi:10.1080/0142159x.2020.1798914

Education for the Anthropocene: Planetary health, sustainable health care, and the health workforce

2020· article· en· W3049124717 on OpenAlexaff
Stefi Barna, Filip Marić, Julia Simons, Shashank Kumar, Peter J. Blankestijn

Bibliographic record

VenueMedical Teacher · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsHealth careWorkforceAnthropoceneInterdependenceCurriculumMandatePopulation healthHealth educationMedicineEngineering ethicsPolitical sciencePsychologySociologyEconomic growthPedagogyEngineeringEnvironmental ethicsSocial scienceEconomics

Abstract

fetched live from OpenAlex

Over the past few centuries, human activity has wrought dramatic changes in the natural systems that support human life. Planetary health is a useful concept for health profession education (HPE) teaching and practice because it situates health within a broader understanding of the interdependent socio-ecological drivers of human and planetary health. It facilitates novel ways of protecting both population health and the natural environment on which human health and well-being depends. This paper focuses on the climate crisis as an example of the relationship between environmental change, healthcare, and education. We analyze how HPE can help decarbonize the healthcare sector to address both climate change and inequity in health outcomes. Based on the healthcare practitioner's mandate of beneficence, we propose simple learning objectives to equip HPE graduates with the knowledge, skills, and values to create a sustainable health system, using carbon emission reductions as an example. These learning objectives can be integrated into HPE without adding unduly to the curriculum load.

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)0.002
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.448
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.356
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations61
Published2020
Admission routes1
Has abstractyes

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