Publication practices of sub-Saharan African Cochrane authors: a bibliometric study
Bibliographic record
Abstract
INTRODUCTION: Cochrane Africa (https://africa.cochrane.org/) aims to increase Cochrane reviews addressing high priority questions in sub-Saharan Africa (SSA). Researchers residing in SSA, despite often drawing on Cochrane methods, training or resources, conduct and publish systematic reviews outside of Cochrane. Our objective was to investigate the extent to which Cochrane authors from SSA publish Cochrane and non-Cochrane reviews. METHODS: We conducted a bibliometric study of systematic reviews and overviews of systematic reviews from SSA, first by identifying SSA Cochrane authors, then retrieving their first and last author systematic reviews and overviews from PubMed (2008 to April 2019) and using descriptive analyses to investigate the country of origin, types of reviews and trends in publishing Cochrane and non-Cochrane systematic reviews over time. To be eligible, a review had to have predetermined objectives, eligibility criteria, at least two databases searched, data extraction, quality assessment and a first or last author with a SSA affiliation. RESULTS: We identified 657 Cochrane authors and 757 eligible systematic reviews. Most authors were from South Africa (n=332; 51%), followed by Nigeria (n=126; 19%). Three-quarters of the reviews (71%) were systematic reviews of interventions. The intervention reviews were more likely to be Cochrane reviews (60.3% vs 39.7%). Conversely, the overviews (23.8% vs 76.2%), qualitative reviews (14.8% vs 85.2%), diagnostic test accuracy reviews (16.1% vs 83.9%) and the 'other' reviews (11.1% vs 88.9%) were more likely to be non-Cochrane reviews. During the study period, the number of non-Cochrane reviews increased more than the number of Cochrane reviews. About a quarter of the reviews covered infectious disease topics. CONCLUSION: Cochrane authors from SSA are increasingly publishing a diverse variety of systematic reviews and overviews of systematic reviews, often opting for non-Cochrane journals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.031 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".