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Record W4295037425 · doi:10.21203/rs.3.rs-1956595/v1

The Cameroon Health Research and Evidence Database (CAMHRED): tools, methods and application of a local evidence mapping initiative

2022· preprint· en· W4295037425 on OpenAlexaff
Clémence Ongolo-Zogo, Hussein El‐Khechen, Frederick Morfaw, Pascal Djiadjeu, Babalwa Zani, Andrea Darzi, Paul Wankah Nji, Agatha Nyambi, Andrea Youta, Faiyaz Zaman, Cheikh Tchouambou Youmbi, Ines Ndzana Siani, Lawrence Mbuagbaw

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsOntario HIV Treatment NetworkUniversité de SherbrookeImpactMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsKnowledge translationLocal languageEvidence-based practiceComputer sciencePrioritizationGrey literatureMEDLINEDatabaseData sciencePolitical scienceMedicineKnowledge managementAlternative medicineBusinessProcess management

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.184
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0270.036
Science and technology studies0.0010.001
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.006

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.

Opus teacher head0.550
GPT teacher head0.577
Teacher spread0.027 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreMethods

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractno

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