Where is students’ research in evidence-informed decision-making in health? Assessing productivity and use of postgraduate students’ research in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Investing in research that is not accessible or used is a waste of resources and an injustice to human subject participants. Post-graduate students' research in institutions of higher learning involves considerable time, effort and money, warranting evaluation of the return on investment. Although individual studies addressing research productivity of post-graduate students are available, a synthesis of these results in low-income settings has not been undertaken. Our first aim is to identify the types of approaches that increase productivity and those that increase the application of medical post-graduate students' research and to assess their effectiveness. Our second aim is to assess the determinants of post-graduate students' research productivity. METHODS: We propose a two-stage systematic review. We will electronically search for published and grey literature in PubMed/MEDLINE and the ERIC databases, as well as contact authors, research administration units of universities, and other key informants as appropriate. In stage one, we will map the nature of the evidence available using a knowledge translation framework adapted from existing literature. We will perform duplicate screening and selection of articles, data abstraction, and risk of bias assessments for included primary studies as described in the Cochrane handbook for systematic reviews. Our primary outcome is publication output as a measure of research productivity, whilst we defined research use as citations in peer-reviewed journals or policy-related documents as our secondary outcome. In stage two, we will perform a structured narrative synthesis of the findings and advance to quantitative meta-analysis if the number of studies are adequate and their heterogeneity is low. Adapting the Grading, Recommendations, Assessment, Development and Evaluation (GRADE) approach, we will assess the overall quality of evidence for effects, and report our results in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. DISCUSSION: We will share our findings with universities, other training institutions, civil society, funders as well as government departments in charge of education and health particularly in low- and middle-income countries.
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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 | 0.196 | 0.533 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".