Establishing a Digital Health Care Ecosystem in a Health Sciences University in South Africa: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Comprehensive and formalized digital health care ecosystems in health sciences tertiary education in South Africa do not currently exist, but they have the potential to influence teaching and learning, research, and community engagement. OBJECTIVE: A total of 3 key objectives underpin the study, that is, determining the health care curriculum needs and required content for the development of a formalized digital health ecosystem, determining the level of readiness of staff and students to participate in a digital health care ecosystem, and determining whether community engagement and strategic partnerships can contribute to the sustainability of a digital health care ecosystem. METHODS: A multipronged approach will be used to address the objectives, with a mixed methods design being undertaken. The qualitative phases will be phenomenological in nature, and triangulation of information along with thematic analysis will be conducted on the collected data. Quantitative data will be collected prospectively and cross-sectionally and analyzed using descriptive analysis. Sampling will include subject experts for the Delphi technique, staff and students at the University, clinical training and education partners, and community leaders. This study has received ethical approval from the Sefako Makgatho Health Sciences University Research and Ethics Committee (SMUREC/H/260/2023:PG). RESULTS: Data collection for the first phase will begin in January 2024 and conclude in December 2024. Phase 2 and 3 of the study will be conducted concurrently, with data collection starting in January 2025 and concluding in December 2026. CONCLUSIONS: The establishment of a digital health care ecosystem has the potential to benefit staff, students, and communities through stakeholder collaboration, educational opportunities, research projects, and improved service delivery. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57821.
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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 | 0.120 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.018 |
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".