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Record W7052207080

Promoting the health of marginalized populations in Ecuador through international collaboration and educational innovations

2009· article· en· W7052207080 on OpenAlexfundaboutno aff

Bibliographic record

VenueUASB-DIGITAL Categories (Universidad Andina Simón Bolívar) · 2009
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesCanadian Institutes of Health ResearchMichael Smith Health Research BCPierre Elliott Trudeau Foundation
KeywordsLatin AmericansSustainabilityTypologyPublic healthCapacity buildingInternational healthTraining (meteorology)Social determinants of health
DOInot available

Abstract

fetched live from OpenAlex

This paper examines two innovative educational initiatives for the Ecuadorian public health workforce: a Canadian-funded Masters programme in ecosystem approaches to health that focuses on building capacity to manage environmental health risks sustainably; and the training of Ecuadorians at the Latin American School of Medicine in Cuba (known as Escuela Latinoamericana de Medicina in Spanish). We apply a typology for analysing how training programmes address the needs of marginalized populations and build capacity for addressing health determinants. We highlight some ways we can learn from such training programmes with particular regard to lessons, barriers and opportunities for their sustainability at the local, national and international levels and for pursuing similar initiatives in other countries and contexts. We conclude that educational efforts focused on the challenges of marginalization and the determinants of health require explicit attention not only to the knowledge, attitudes and skills of graduates but also on effectively engaging the health settings and systems that will reinforce the establishment and retention of capacity in low- and middle-income settings where this is most needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.269
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2009
Admission routes2
Has abstractyes

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