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Record W4313597297 · doi:10.1016/s2542-5196(22)00218-2

The World Organization of Family Doctors Air Health Train the Trainer Program: lessons learned and implications for planetary health education

2023· review· en· W4313597297 on OpenAlexafffund
Alice McGushin, Enrique Barros, Mayara Floss, Yousser Mohammad, Achiri E Ndikum, Christophe Ngendahayo, Peter A Oduor, Sadia Sultana, Rachel Wong, Alan Abelsohn

Bibliographic record

VenueThe Lancet Planetary Health · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of British ColumbiaYork UniversityArray BioPharmaNYU Grossman School of MedicineColumbia UniversityCHEST Foundation
KeywordsTrainerMedical educationTraining (meteorology)Intersection (aeronautics)Health educationPublic relationsPsychologyMedicinePolitical scienceNursingEngineeringPublic healthComputer scienceTransport engineeringGeography

Abstract

fetched live from OpenAlex

The World Organization of Family Doctors (WONCA) Air Health Train the Trainer Program was a pilot educational programme that focused on a key aspect of planetary health: the intersection of air pollution, human health, and climate change. In this Viewpoint, we-the coordinators of the training programme and some of the most active trainers-briefly describe the programme and discuss implementation successes, challenges, and lessons learned, which relate to the creation and use of training materials appropriate for health professionals in low-income and middle-income countries, strategies to improve the retention of trainers to deliver activities in their communities, and the development of stronger networks and further tools to support trainers. These findings could be applied to future education and training programmes.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.179
GPT teacher head0.420
Teacher spread0.241 · 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
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

Citations16
Published2023
Admission routes2
Has abstractyes

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