Notice bibliographique
Résumé
Introduction Midwives play a pivotal role in maternal health, providing essential care that spans the continuum of pregnancy, childbirth, and the postpartum period. Nevertheless, they are frequently absent from policy dialogue, which prevents their voices and expertise from influencing healthcare planning and policymaking. Providing career opportunities for midwives and increasing their participation in leadership roles are potential strategies to promote increased availability and may contribute to enhanced quality of maternal health care. This thesis aims to gain insights into if and how training programmes and training institutions influence midwives’ careers, and consequently their distribution in Mali. The main research question of this thesis was: How do changes in midwifery education programmes influence midwives’ careers in maternal health in Mali? Study design and methods The research for the thesis used a case study approach employing mixed method research with both qualitative and quantitative data collection methods, as well as a literature and document review. Key Findings The thesis is divided into two parts. Part 1 is a situation analysis regarding midwives’ existing employment situation, particularly in rural areas and primary-level care services. Part 2 focuses on the influence of improvements in training programmes. In general, our findings related to Part 1 – the situation analysis – indicated midwives’ disaffection from the rural environment and first-level services. We also identified the important roles of personal characteristics, professional aspirations and career development, and working conditions in their decision-making. They are driven by their ambitions and are therefore willing to take more responsibility for their career development. The results of the study on the career trajectory of midwives showed that among our respondents, very few moved up within the health sector hierarchy (vertical mobility). Opportunities to obtain managerial positions, and therefore involvement in policy decisions including sexual, reproductive, maternal, neonatal and adolescent health (SRMNAH) policymaking, were low. The most prevalent mobility among our sample of midwives was horizontal, involving moving between equivalent posts in different structures. Gender was found to play a significant role in midwives' ability to move for work, impacting on their horizontal physical mobility. The organisation of training programmes and the location of training institutions have an impact on midwives’ career choices and working environments. The probability of choosing to work in rural areas or to stay employed in a rural area was found to be enhanced by the notion of a ‘rural pipeline’. The results of Part 2 of the research showed that the changes in education programmes had an influence on the career prospects of midwives. The content, the formulation and the adoption process of policy changes in training programmes resulted in fewer employment opportunities for graduates due to non-adherence to, and non-appropriation of, the policy by relevant stakeholders. On the other hand, the provision of training at the Master’s level offered greater career perspectives for some midwives. The acquisition of new skills in research, management and evaluation allowed for professional development perspectives, the absence of which is seen as one of the main obstacles to motivation and retention of midwives in the SRMNAH field. This study also highlights the need to integrate changes in training programmes into a broader human resource development perspective, aiming to respond to the needs of the health system in addition to the personal needs of the health workers. Conclusion Alterations to training programmes can impact the careers of midwives in Mali. However, for these changes to be effective, it is essential that the Ministry of Health is involved in the development and accreditation of training programmes. This ensures that the training programmes align closely with the current health needs and policies.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».