Effective supervision of doctoral students in public and population health in Africa: CARTA supervisors’ experiences, challenges and perceived opportunities
Bibliographic record
Abstract
The quality and success of postgraduate education largely rely on effective supervision. Since its inception in 2008, the Consortium for Advanced Research Training in Africa (CARTA) has been at the forefront of providing training to both students and supervisors in the field of public and population health. However, there are few studies on supervisors' perceptions on effective doctoral supervision. We used a mostly descriptive study design to report CARTA-affiliated doctoral supervisors' reflections and perceptions on doctoral supervision, challenges and opportunities. A total of 77 out of 160 CARTA supervisors' workshop participants responded to the evaluation. The respondents were affiliated with 10 institutions across Africa. The respondents remarked that effective supervision is a two-way process, involving both supervisor and supervisee's commitment. Some reported that the requirements for effective supervision included the calibre of the PhD students, structure of the PhD programme, access to research infrastructure and resources, supervision training, multidisciplinary exposure and support. Male supervisors have significantly higher number of self-reported PhD graduates and published articles on Scopus but no difference from the females in h-index. We note both student and systemic challenges that training institutions may pursue to improve doctoral supervision in Africa.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".