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Record W2317224876 · doi:10.1136/bmjinnov-2015-000045

Innovating to improve primary care in less developed countries: towards a global model

2015· review· en· W2317224876 on OpenAlexafffund
Lara Fairall, Eric D. Bateman, Ruth Cornick, Gill Faris, Venessa Timmerman, Naomi Folb, Max Bachmann, Merrick Zwarenstein, Richard Smith

Bibliographic record

VenueBMJ Innovations · 2015
Typereview
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsWestern University
FundersNational Institute of Mental HealthMedical Research CouncilU.S. Department of Health and Human ServicesNational Institutes of HealthUnited States Agency for International DevelopmentInternational Development Research CentreHealth Resources and Services AdministrationU.S. President’s Emergency Plan for AIDS Relief
KeywordsDeveloping countryUnit (ring theory)SustainabilityGlobal healthBusinessNursingHealth careMedicineEconomic growthPsychologyPublic healthEconomics

Abstract

fetched live from OpenAlex

One of the biggest problems in global health is the lack of well trained and supported health workers in less developed settings. In many rural areas there are no physicians, and it is important to find ways to support and empower nurses and other health workers. The Knowledge Translation Unit of the University of Cape Town Lung Institute has spent 14 years developing a series of innovative packages to support and empower nurses and other health workers. PACK (Practical Approach to Care Kit) Adult comprises policy-based and evidence-informed guidelines; onsite, team and case-based training; non-physician prescribing; and a cascade system of scaling up. A series of randomised trials has shown the effectiveness of the packages, and methods are now being developed to respond cost-effectively and sustainably to global demand for implementing PACK Adult. Global health would probably benefit from less time and money spent developing new innovations and more spent on finding ways to spread those we already have.

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 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)0.020
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.140
GPT teacher head0.449
Teacher spread0.309 · 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.

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

Citations72
Published2015
Admission routes2
Has abstractyes

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