Integrated Decentralized Training for Health Professions Education at the University of KwaZulu-Natal, South Africa: Protocol for the I-DecT Project
Bibliographic record
Abstract
BACKGROUND: The Integrated Decentralized Training (i-DecT) project was created to address the current need for health care in South Africa among resource poor climates in rural and periurban settings. The University of KwaZulu-Natal (UKZN) in South Africa has embarked on a program within the School of Health Sciences (SHS) to decentralize the clinical learning platform in order to address this disparity. Framed in a pragmatic stance, this proposal is geared towards informing the roll out of decentralized clinical training (DCT) within the province of KwaZulu-Natal. There currently remains uncertainty as to how the implementation of this program will unfold, especially for the diverse SHS, which includes specialities like audiology, dentistry, occupational therapy, optometry, pharmacy, physiotherapy, speech-language pathology, and sport science. Consequently, there is a need to carefully monitor and manage this DCT in order to ensure that the participating students have a positive learning experience and achieve expected academic outcomes, and that the needs of the communities are addressed adequately. OBJECTIVE: The study aims to explore the factors that will influence the roll-out of the DCT by developing an inclusive and context-specific model that will adhere to the standards set by the SHS for the DCT program at UKZN. METHODS: Key role players, including but not limited to, the South African Ministry of Health policy makers, clinicians, policy makers at UKZN, clinical educators, academicians, and students of UKZN within the SHS will participate in this project. Once the infrastructural, staffing and pedagogical enablers and challenges are identified, together with a review of existing models of decentralized training, a context-specific model for DCTl will be proposed based on initial pilot data that will be tested within iterative cycles in an Action Learning Action Research (ALAR) process. RESULTS: The study was designed to fit within the existing structures, and emerging framework and memorandum of understanding between the partners of this initiative, namely, the Ministry of Health and UKZN in order to develop health care professionals that are competent and prepared for the changing dynamics of healthcare in a developing world. CONCLUSIONS: It is envisioned that this study, the first to include a combination of health professionals in a DCT platform at UKZN, will not only contribute to effective service delivery, but may also serve to promote an interprofessional cooperation within the SHS and tertiary institutions in similar settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.107 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".