The Cameroon Health Research and Evidence Database (CAMHRED): tools and methods for local evidence mapping
Bibliographic record
Abstract
BACKGROUND: Local evidence is important for contextualized knowledge translation. It can be used to adapt global recommendations, to identify future research priorities and inform local policy decisions. However, there are challenges in identifying local evidence in a systematic, comprehensive, and timely manner. There is limited guidance on how to map local evidence and provide it to users in an accessible and user-friendly way. In this study, we address these issues by describing the methods for the development of a centralized database of health research evidence for Cameroon and its applications for research prioritization and decision making. METHODS: We searched 10 electronic health databases and hand-searched the archives of non-indexed African and Cameroonian journals. We screened titles, abstracts, and full texts of peer reviewed journal articles published between 1999 and 2019 in English or French that assess health related outcomes in Cameroonian populations. We extracted relevant study characteristics based on a pre-established guide. We developed a coding scheme or taxonomy of content areas so that local evidence is mapped to corresponding domains and subdomains. Pairs of reviewers coded articles independently and resolved discrepancies by consensus. Moreover, we developed guidance on how to search the database, use search results to create evidence maps and conduct knowledge gap analyses. RESULTS: The Cameroon Health Research and Evidence Database (CAMHRED) is a bilingual centralized online portal of local evidence on health in Cameroon from 1999 onwards. It currently includes 4384 studies categorized into content domains and study characteristics (design, setting, year and language of publication). The database is searchable by keywords or through a guided search. Results including abstracts, relevant study characteristics and bibliographic information are available for users to download. Upon request, guidance on how to optimize search results for applications like evidence maps and knowledge gap analyses is also available. CONCLUSIONS: CAMHRED ( https://camhred.org/ ) is a systematic, comprehensive, and centralized resource for local evidence about health in Cameroon. It is freely available to stakeholders and provides an additional resource to support their work at various levels in the research process.
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 | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.232 | 0.478 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.128 | 0.107 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".