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Record W2979964001 · doi:10.1136/bmjgh-2019-001533

Towards a regional strategy for resolving the human resources for health challenges in Africa

2019· article· en· W2979964001 on OpenAlexaff
James Avoka Asamani, Oladele Akogun, Jennifer Nyoni, Adam Ahmat, Juliet Nabyonga‐Orem, Prosper Tumusiime

Bibliographic record

VenueBMJ Global Health · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsInnovation Cluster (Canada)
FundersWorld Health Organization
KeywordsDistribution (mathematics)Human resourcesBusinessResource distributionResource (disambiguation)Production (economics)Resource allocationEconomic growthEnvironmental resource managementPolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

The human resource for health (HRH) in sub-Saharan Africa is neither fit for purpose, sufficient nor distributed equitably to meet the region's universal health coverage (UHC) needs. A regional strategy is required to replace the current inefficient, compartmentalised and ineffective approach that breeds inequity and extracontinental resource drain. We propose a regional approach to HRH planning, needs mapping, production and distribution to meet the region's requirement for UHC.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.429
Teacher spread0.298 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations53
Published2019
Admission routes1
Has abstractyes

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