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Enregistrement W2508975878 · doi:10.1186/s12960-016-0150-7

Impact of MPH programs: contributing to health system strengthening in low- and middle-income countries?

2016· article· en· W2508975878 sur OpenAlexfundno aff
Prisca Zwanikken, Lucy Alexander, Albert Scherpbier

Notice bibliographique

RevueHuman Resources for Health · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensnon disponible
Organismes subventionnairesNewfoundland and Labrador
Mots-clésWorkforcePublic healthContext (archaeology)Health promotionHealth administrationHealth services researchFocus groupPromotion (chess)Public relationsMedicineNursingMedical educationPsychologyPolitical scienceBusinessMarketingGeography

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The "health workforce" crisis has led to an increased interest in health professional education, including MPH programs. Recently, it was questioned whether training of mid- to higher level cadres in public health prepared graduates with competencies to strengthen health systems in low- and middle-income countries. Measuring educational impact has been notoriously difficult; therefore, innovative methods for measuring the outcome and impact of MPH programs were sought. Impact was conceptualized as "impact on workplace" and "impact on society," which entailed studying how these competencies were enacted and to what effect within the context of the graduates' workplaces, as well as on societal health. METHODS: This is part of a larger six-country mixed method study; in this paper, the focus is on the qualitative findings of two English language programs, one a distance MPH program offered from South Africa, the other a residential program in the Netherlands. Both offer MPH training to students from a diversity of countries. In-depth interviews were conducted with 10 graduates (per program), working in low- and middle-income health systems, their peers, and their supervisors. RESULTS: Impact on the workplace was reported as considerable by graduates and peers as well as supervisors and included changes in management and leadership: promotion to a leadership position as well as expanded or revitalized management roles were reported by many participants. The development of leadership capacity was highly valued amongst many graduates, and this capacity was cited by a number of supervisors and peers. Wider impact in the workplace took the form of introducing workplace innovations such as setting up an AIDS and addiction research center and research involvement; teaching and training, advocacy, and community engagement were other ways in which graduates' influence reached a wider target grouping. Beyond the workplace, an intersectoral approach, national reach through policy advisory roles to Ministries of Health, policy development, and capacity building, was reported. Work conditions and context influenced conduciveness for innovation and the extent to which graduates were able to have effect. Self-selection of graduates and their role in selecting peers and supervisors may have resulted in some bias, some graduates could not be traced, and social acceptability bias may have influenced findings. CONCLUSIONS: There was considerable impact at many levels; graduates were perceived to be able to contribute significantly to their workplaces and often had influence at the national level. Much of the impact described was in line with public health educational aims. The qualitative method study revealed more in-depth understanding of graduates' impact as well as their career pathways.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,990
Score d'incertitude au seuil0,054

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,019
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0040,003
Science ouverte0,0010,006
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,337
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
DomaineÉvaluation
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations26
Publié2016
Routes d'admission1
Résumé présentoui

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