MétaCan
Menu
Back to cohort
Record W2615125647

Promoting the Health of Marginalized Populations in Ecuador through International Collaboration and Educational innovations/Promouvoir la Sante Des Populations Marginalisees En Equateur a Travers la Collaboration Internationale et Des Innovations En Matiere De formation/Promocion De la Salud De Poblaciones Marginadas En El Ecuador Mediante la Colaboracion Internacional E Innovaciones Educativas

2009· article· es· W2615125647 on OpenAlexaboutno aff
Margot W. Parkes, Jerry Spiegel, Jaime Breilh, Fabio Sánchez, Robert Huish, Annalee Yassi

Bibliographic record

VenueBulletin of the World Health Organization · 2009
Typearticle
Languagees
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforcePublic healthEconomic growthInternational healthSustainabilityCapacity buildingPolitical sciencePublic relationsHealth promotionMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Introduction Much attention has been paid to the pronounced shortage of health workers in low- and middle-income countries (LMICs). (1-3) In addition, greater recognition of interrelated determinants of health suggests that personnel with new skills must be added to the mix of human resources mobilized to improve health. Nevertheless, there is little evidence that training programmes for LMIC health personnel are meeting this challenge. Furthermore, the way that international assistance is provided to assist education of health workers may be contributing as much to the problem as providing solutions. To examine this concern, we studied two post-secondary educational initiatives for the Ecuadorian health workforce: a Canadian-funded Masters Programme in Ecosystem Approaches to Health (MEAH) that focuses on building capacity to sustainably manage environmental health risks; (4) and the training of Ecuadorians at the Latin American School of Medicine in Cuba (ELAM--using the acronym from the Spanish name Escuela Latinoamericana de Medicina). (5) We suggest a typology to guide analysis of challenges and gaps. We then consider key elements for learning from such programmes with particular regard to lessons, barriers and opportunities at the local, national and international level. Training to meet the needs of marginalized In reviewing challenges in building a global public health workforce, Beaglehole & Dal Poz drew attention to the limitations of traditional approaches to public health education, which include narrow disciplinary focus, isolation from field experience, overly medicalized orientations and weak incentives to work in LMIC settings where need is greatest. (6) In keeping with the framing of the public health workforce as those who are primarily involved in protecting and promoting the health of whole or specific populations [emphasis added], (6) we concentrate on the challenge of educating health workers whose mandate is to focus on marginalized communities. In doing so we recognize the inevitable tensions and controversies in describing specific as marginalized, or vulnerable, and the dual importance of recognizing the assets and capacity of such communities as well as the structural power differentials and processes of exclusion that drive health inequities from global and local levels. (7-11) With these challenges in mind, we suggest a typology for training programmes in LMICs (Table 1) that points to where greater attention is needed to equip graduates with specific capabilities to address: (i) determinants of health to complement skills necessary for delivery of clinical services; and (ii) the needs of marginalized that are particularly vulnerable to poor health conditions, status and services and other manifestations of structural inequities. In the context of our typology, MEAH is explicitly oriented to building skills for addressing health determinants that affect vulnerable communities. On the other hand, ELAM focuses on providing clinical health services to disadvantaged populations, but in a context that is sensitive to health determinants. Examining these two examples in the Ecuadorian context, we argue that a range of training innovations is required to create a public health workforce capable of responding to emerging challenges. Health inequities experienced by marginalized communities in Ecuador are exacerbated by socioeconomic trends, including growing income inequalities. This is illustrated by an increase in the Gini coefficient (where a score of 0 indicates perfectly equal income distribution and 1 complete inequality) from 0.54 in 1995 to 0.59 in 1999. (12) Research in the past decade has also drawn attention to a range of global and local driving forces (such as expansion of the petroleum, mining and agro-industrial sectors) with worrying implications for social and environmental conditions in Ecuador. …

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.037
GPT teacher head0.413
Teacher spread0.376 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of the World Health OrganizationSame topicPublic Health Policies and EducationFrench-language works237,207