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Record W2166240380 · doi:10.4314/ahs.v12i3.11

A systematic review evaluating the impact of task shifting on access to antiretroviral therapy in sub-Saharan Africa

2013· review· en· W2166240380 on OpenAlexafffund
Connor A. Emdin, Peggy Millson

Bibliographic record

VenueAfrican Health Sciences · 2013
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicineRandomized controlled trialPsychological interventionTask (project management)Antiretroviral therapySystematic reviewHuman immunodeficiency virus (HIV)MEDLINEFamily medicineNursingViral loadSurgeryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Task shifting, defined for this review as the shifting of ART initiation and management from physicians to nurses, has been proposed as a possible method to increase access to HIV treatment in Sub-Saharan Africa. OBJECTIVE: To critically evaluate the literature on task shifting, determining if there is evidence to support this view. METHODS: A systematic search of the literature was undertaken, with both peer reviewed publications and conference abstracts presenting original data eligible for inclusion. Studies were evaluated according to methodology and discussion of confounding factors. RESULTS: We identified 25 articles which evaluated the effect of task shifting on access to ART. The evidence was mixed. Although there is a significant body of field reports indicating that task shifting increases access, these studies were of low methodological quality. The only randomized controlled trial included in this review did not find that task shifting increased in access. CONCLUSION: Task shifting appears to be most effective at increasing access when combined with other interventions and financial support. There is a need for more research into the effects of task shifting policies, especially randomized controlled trials and high quality cohort studies.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.499
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.285
GPT teacher head0.556
Teacher spread0.270 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations33
Published2013
Admission routes2
Has abstractyes

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