Evidence-based biomedical research in Sub-Saharan Africa: how library and information science professionals contribute to systematic reviews and meta-analyses
Bibliographic record
Abstract
OBJECTIVE: This study investigated the contributions of library and information science (LIS) professionals to systematic reviews and meta-analyses with authors from Sub-Saharan Africa. It also investigated how the first author's address and type of collaboration affected the involvement of LIS professionals in systematic reviews and meta-analyses. METHODS: Bibliographic data of systematic reviews with author(s) from the forty-six Sub-Saharan African countries was retrieved from MEDLINE. Content and bibliometric analyses were performed on the systematic reviews' full-texts and bibliographic data, respectively, to identify the contributions of LIS professionals and collaboration patterns. RESULTS: Beyond traditional roles as search strategy developers and searchers, the LIS professionals participated in article retrieval, database selection, reference management, draft review, review conceptualization, manuscript writing, technical support, article screening and selection, data extraction, abstract review, and training/teaching. Of the 2,539 publications, LIS professionals were mentioned in 472 publications. LIS professionals from only seven of the forty-six Sub-Saharan African countries were noted to have contributed. LIS professionals from South Africa were mentioned most frequently-five times more than those from other Sub-Saharan African countries. LIS professionals from Sub-Saharan Africa mostly contributed to publications with first authors from Sub-Saharan Africa (90.20%) and intra-Sub-Saharan African collaboration (61.66%). Most LIS professionals (97.91%) that contributed to international collaboration publications were from outside Sub-Saharan Africa. CONCLUSION: The contribution of LIS professionals in Sub-Saharan Africa to evidence-based biomedical research can improve through training, mentoring, and collaboration between LIS associations in Sub-Saharan Africa and those in countries with resources and a history of research collaboration with the region.
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
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 | 0.269 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.042 | 0.047 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".