‘Why do an MPH?’ Motivations and intentions of physicians undertaking postgraduate public health training at the University of Cape Town
Bibliographic record
Abstract
BACKGROUND: Public health (PH) approaches underpin the management and transformation of health systems in low- and middle-income countries. Despite the Master of Public Health (MPH) rarely being a prerequisite for health service employment in South Africa, many physicians pursue MPH qualifications. OBJECTIVES: This study identifies their motivations and career intentions and explored MPH programme strengths and gaps in under- and post-graduate PH training. DESIGN: A cross-sectional study using an online questionnaire was completed by physicians graduating with an MPH between 2000 and 2009 and those enrolled in the programme in 2010 at the University of Cape Town. RESULTS: Nearly a quarter of MPH students were physicians. Of the 65 contactable physicians, 48% responded. They were mid-career physicians who wished to obtain research training (55%), who wished to gain broader perspectives on health (32%), and who used the MPH to advance careers (90%) as researchers, policy-makers, or managers. The MPH widened professional opportunities, with 62% changing jobs. They believed that inadequate undergraduate exposure should be remedied by applying PH approaches to clinical problems in community settings, which would increase the attractiveness of postgraduate PH training. CONCLUSIONS: The MPH allows physicians to transition from pure clinical to research, policy and/or management work, preparing them to innovate changes for effective health systems, responsive to the health needs of populations. Limited local job options and incentives are important constraining factors. Advocacy for positions requiring qualifications and benchmarking exit competencies of programmes nationally may promote enrolment.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".