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Task shifting and task sharing in the health sector in sub-Saharan Africa: evidence, success indicators, challenges, and opportunities

2023· review· en· W4386611302 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePan African Medical Journal · 2023
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsTask (project management)WorkforceSustainabilityEconomic shortageMedicineTask forceScale (ratio)Process managementSupply chainKnowledge managementPublic relationsBusinessMarketingEconomic growthComputer scienceEconomicsPolitical scienceManagementGovernment (linguistics)

Abstract

fetched live from OpenAlex

This review explores task shifting and task sharing in sub-Saharan African healthcare to address workforce shortages and cost-effectiveness. Task shifting allocates tasks logically, while task sharing involves more workers taking on specific duties. Challenges include supply chain issues, pay inadequacy, and weak supervision. Guidelines and success measures are lacking. Initiating these practices requires evaluating factors and ensuring sustainability. Task shifting saves costs but needs training and support. Task sharing boosts efficiency, enabling skilled clinicians to contribute effectively. To advance task shifting and sharing in the region, further research is needed to scale up effective initiatives. Clear success indicators, monitoring, evaluation, and learning plans, along with exploration of sustainability and appropriateness dimensions, are crucial elements to consider.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.199
GPT teacher head0.377
Teacher spread0.178 · 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