Human resources for health interventions in high- and middle-income countries: findings of an evidence review
Notice bibliographique
Résumé
Many high- and middle-income countries face challenges in developing and maintaining a health workforce which can address changing population health needs. They have experimented with interventions which overlap with but have differences to those documented in low- and middle-income countries, where many of the recent literature reviews were undertaken. The aim of this paper is to fill that gap. It examines published and grey evidence on interventions to train, recruit, retain, distribute, and manage an effective health workforce, focusing on physicians, nurses, and allied health professionals in high- and middle-income countries. A search of databases, websites, and relevant references was carried out in March 2019. One hundred thirty-one reports or papers were selected for extraction, using a template which followed a health labor market structure. Many studies were cross-cutting; however, the largest number of country studies was focused on Canada, Australia, and the United States of America. The studies were relatively balanced across occupational groups. The largest number focused on availability, followed by performance and then distribution. Study numbers peaked in 2013-2016. A range of study types was included, with a high number of descriptive studies. Some topics were more deeply documented than others-there is, for example, a large number of studies on human resources for health (HRH) planning, educational interventions, and policies to reduce in-migration, but much less on topics such as HRH financing and task shifting. It is also evident that some policy actions may address more than one area of challenge, but equally that some policy actions may have conflicting results for different challenges. Although some of the interventions have been more used and documented in relation to specific cadres, many of the lessons appear to apply across them, with tailoring required to reflect individuals' characteristics, such as age, location, and preferences. Useful lessons can be learned from these higher-income settings for low- and middle-income settings. Much of the literature is descriptive, rather than evaluative, reflecting the organic way in which many HRH reforms are introduced. A more rigorous approach to testing HRH interventions is recommended to improve the evidence in this area of health systems strengthening.
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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 | 0,030 | 0,095 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,014 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».