Support mechanisms for research generation and application for postgraduate students in four universities in Uganda
Bibliographic record
Abstract
BACKGROUND: A large proportion of postgraduate students the world over complete a research thesis in partial fulfilment of their degree requirements. This study identified and evaluated support mechanisms for research generation and utilization for masters' students in health institutions of higher learning in Uganda. METHODS: This was a self-administered cross-sectional survey using a modified self-assessment tool for research institutes (m-SATORI). Postgraduate students were randomly selected from four medical or public health Ugandan universities at Makerere, Mbarara, Nkozi and Mukono and asked to circle the most appropriate response on a Likert scale from 1, where the "situation was unfavourable and/or there was a need for an intervention", to 5, where the "situation was good or needed no intervention". These questions were asked under four domains: the research question; knowledge production, knowledge transfer and promoting use of evidence. Mean scores of individual questions and aggregate means under the four domains were computed and then compared to identify areas of strengths and gaps that required action. RESULTS: Most of the respondents returned their questionnaires, 185 of 258 (71.7%), and only 79 of these (42.7%) had their theses submitted for examination. The majority of the respondents were male (57.3%), married or cohabiting (58.4%), and were medical doctors (71.9%) from Makerere University (50.3%). The domain proposal development for postgraduate research project had the highest mean score of 3.53 out of the maximum 5. Three of the four domains scored below the mid-level domain score of 3, that is, the situation is neither favourable nor unfavourable. Areas requiring substantial improvements included priority-setting during research question identification, which had the lowest mean score of 2.12. This was followed by promoting use of postgraduate research products, tying at mean scores of 2.28 each. The domain knowledge transfer of postgraduate research products had an above-average mean score of 2.75. CONCLUSIONS: This study reports that existing research support mechanisms for postgraduate students in Uganda encourage access to supervisors and mentors during proposal development. Postgraduate students' engagement with research users was limited in priority-setting and knowledge transfer. Since supervisors and mentors views were not captured, future follow-on research could tackle this aspect.
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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 | 0.074 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".