Academic research productivity of post-graduate students at Makerere University College of Health Sciences, Uganda, from 1996 to 2010: a retrospective review
Bibliographic record
Abstract
BACKGROUND: Research is a core business of universities globally, and is crucial in the scientific process as a precursor for knowledge uptake and use. We aimed to assess the academic productivity of post-graduate students in a university located in a low-income country. METHODS: This is an observational retrospective documentary analysis using hand searching archives, Google Scholar and PubMed electronic databases. The setting is Makerere University College of Health Sciences, Uganda. Records of post-graduate students (Masters) enrolled from 1996 to 2010, and followed to 2016 for outcomes were analysed. The outcome measures were publications (primary), citations, electronic dissertations found online or conference abstracts (secondary). Descriptive and multivariable logistic regression analyses were performed using Stata 14.1. RESULTS: We found dissertations of 1172 Masters students over the 20-year period of study. While half (590, 50%) had completed clinical graduate disciplines (surgery, internal medicine, paediatrics, obstetrics and gynaecology), Master of Public Health was the single most popular course, with 393 students (31%). Manuscripts from 209 dissertations (18%; 95% CI, 16-20%) were published and approximately the same proportion was cited (196, 17%; 95% CI, 15-19%). Very few (4%) policy-related documents (technical reports and guidelines) cited these dissertations. Variables that remained statistically significant in the multivariable model were students' age at enrolment into the Masters programme (adjusted coefficient -0.12; 95% CI, -0.18 to -0.06; P < 0.001) and type of research design (adjusted coefficient 0.22; 0.03 to 0.40; P = 0.024). Cohort studies were more likely to be published compared to cross-sectional designs (adjusted coefficient 0.78; 95% CI, 0.2 to 1.36; P = 0.008). CONCLUSIONS: The productivity and use of post-graduate students' research conducted at the College of Health Sciences Makerere University is considerably low in terms of peer-reviewed publications and citations in policy-related documents. The need for effective strategies to reverse this 'waste' is urgent if the College, decision-makers, funders and the Ugandan public are to enjoy the 'return on investment' from post-graduate students research.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".