IDEAL: Maintaining PHC-focused training in a MBChB programme through a COVID-induced innovation
Bibliographic record
Abstract
Responding to the need for authentic clinical training for students in the context of coronavirus disease 2019 (COVID-19), the Stellenbosch University Faculty of Medicine and Health Sciences developed an innovative 12-week longitudinal, integrated rotation for pre-final-year medical students, the Integrated Distributed Engagement to Advance Learning (IDEAL) rotation. This saw 252 students being placed across 30 primary and secondary healthcare facilities in the Western and Northern Cape provinces. With a focus on service learning, the rotation was built on experiences and research of members of the planning team, as well as partnership relationships developed over an extended period. The focus of student learning was on clinical reasoning through being exposed to undifferentiated patient encounters and the development of practical clinical skills. Students on the distributed platform were supported by clinicians on site, alongside whom they worked, and by a set of online supports, in the form of resources placed on the learning management systems, learning facilitators to whom patient studies were submitted and wellness supporters. Important innovations of the rotation included extensive distribution of clinical training, responsiveness to health service need, co-creation of the module with students, the roles of learning facilitators and wellness supporters, the use of mobile apps and the integration of previously siloed learning outcomes. The IDEAL rotation was seen to be so beneficial as a learning experience that it has been incorporated into the medical degree on an ongoing basis.Contribution: Longitudinal exposure of students to undifferentiated patients in a primary health care context allows for integrated, self-regulated learning. This provides excellent opportunities for medical students, with support, to develop both clinical reasoning and practical skills.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".