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
Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».