The Durban University of Technology Faculty of Health Sciences Decentralized Clinical Training Project: Protocol for an Implementation Study in KwaZulu-Natal, South Africa
Notice bibliographique
Résumé
BACKGROUND: The Durban University of Technology (DUT) Faculty of Health Sciences (FHS) in KwaZulu-Natal, South Africa, is embarking on a project to implement a Decentralized Clinical Training Program (DCTP). The DUT FHS DCTP project is being conducted in response to the growing demands of students requiring clinical service placements as part of work-integrated learning. The project is also geared toward responding to existing gaps in current practices related to the implementation of a DCTP, which has mainly been through traditional universities providing training to medical, optometry, occupational therapy, and physiotherapy students. In South Africa, a DCTP is yet to be implemented within the context of a university of technology; it is yet to be implemented within health science faculties that offer undergraduate health science programs in mainstream biomedicine and alternative and complementary disciplines. OBJECTIVE: We aim to design, pilot, and establish an effective DCTP at the DUT FHS in KwaZulu-Natal, South Africa. METHODS: Participatory action research comprising various designs-namely, appreciative inquiry, qualitative case study design, phenomenography, and descriptive qualitative study design-will be used to conduct the study. Data will be collected using individual interviews, focus group discussions, nominal group technique, consensus methodology, and narrative inquiry. Study participants will include various internal and external stakeholders of the DUT, namely, academic staff; students; key informants from universities currently using successfully established DCTPs; academic support staff; staff working in human resources, finance, procurement, and accounting; and experts in other disciplines such as engineering and information systems. Overall, 4 undergraduate health science programs-namely, Radiography, Medical Orthotics and Prosthetics, Clinical Technology, and Emergency Medical Care and Rescue-will be part of the project's pilot phase. Findings from the project's pilot phase will be used to inform scale-up in the other undergraduate programs in the DUT FHS. The project is being implemented as part of the university's strategic objective of devising innovative curricula and pedagogical practices to improve the mastery, skill set, and competence of health science graduates. RESULTS: The study has currently commenced with the situational analysis, consisting of engagement with external stakeholders implementing DCTPs. The data to be generated from the completion of the situational analysis are anticipated to be published in 2024. CONCLUSIONS: This project is envisioned to facilitate collaboration among the universities of technology, traditional universities, Ministry of Health, and private sector for clinical placement of undergraduate health science students in health establishments that are away from the university, thereby exposing them to real-life experiences related to health care. This will facilitate authentic learning experiences that will contribute to improved competencies of graduates in relation to the health needs of society and the multiple realities of the South African health system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52243.
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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 | 0,124 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,063 | 0,012 |
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 ».