Working towards universal health coverage: a qualitative study to identify strategies for improving student enrolment for the pre-service training of nurses, midwives and community health workers in Nigerian health training institutions
Notice bibliographique
Résumé
BACKGROUND: Student enrolment processes and practices can affect the quality of pre-service training programmes. These processes and practices may have serious implications for the quality and quantity of students within health training institutions, the quality of education for prospective health workers and consequently health workforce performance. This study assessed current student enrolment processes and practices for nurses, midwives and community health workers within health training institutions in two Nigerian states, so as to identify strategies for improving student enrolment for these key cadres of frontline health workers. METHODS: This study was carried out in Bauchi and Cross-River States, which are the two Human Resources for Health (HRH) project focal states in Nigeria. Utilizing a qualitative research design, 55 in-depth interviews and 13 focus group discussions were conducted with key stakeholders including students and tutors from pre-service health training institutions as well as policy-makers and public sector decision-makers from Ministries of Health, Government Agencies and Regulatory Bodies. Study participants were purposively sampled and the qualitative data were audio-recorded, transcribed and then thematically analysed. RESULTS: Study participants broadly described the application process to include the purchase, completion and submission of application forms by prospective students prior to participation in entrance examinations and oral interviews. The use of 'weeding examinations' during the student enrolment process, especially in Bauchi state, was identified as a useful quality assurance mechanism for the pre-service training programmes of frontline health workers. Other strategies identified by stakeholders to address challenges with student enrolment include sustained advocacy to counter-cultural norms and gender stereotypes vis-à-vis certain professions, provision of scholarships for trainee frontline health workers and ultimately the development as well as effective implementation of national and state-specific policy and implementation guidelines for the student enrolment of key frontline health workers. CONCLUSION: While there are challenges which currently affect student enrolment for nurses, midwives and community health workers in Nigeria, this study has proposed key strategies which if carefully considered and implemented can substantially improve the status quo. These will probably have far-reaching implications for improving health workforce performance, population health outcomes and efforts to achieve universal health coverage.
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 | 0,015 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».