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Record W4401815142 · doi:10.1016/j.lansea.2024.100463

Strengthening primary health care through community health workers in South Asia

2024· review· en· W4401815142 on OpenAlexaff
Prakriti Shrestha, Kaosar Afsana, Manuj C. Weerasinghe, Henry B. Perry, Harsha Joshi, Nisha Rana, Zahid Memon, Nazrana Khaled, Sumit Malhotra, Surbhi Bhardwaj, Simrin Kafle, Yoko Inagaki, Austin Schimdt, Stephen Hodgins, Dinesh Neupane, Krishna D. Rao

Bibliographic record

VenueThe Lancet Regional Health - Southeast Asia · 2024
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrimary health carePrimary careCommunity health workersCommunity healthHealth careEnvironmental healthMedicineNursingEconomic growthFamily medicineHealth servicesPublic healthEconomics

Abstract

fetched live from OpenAlex

The growing health challenges in South Asia require further adaptations of community health worker (CHW) programs as a key element of primary health care (PHC). This paper provides a comparative analysis of CHW programs in five countries (Bangladesh, India, Nepal, Pakistan, and Sri Lanka), examines successes and challenges, and suggests reforms to better ensure highly performing CHW programs. To examine CHW programs in the region, we conducted a narrative review of the peer-reviewed and grey literatures, as well as eliciting opinions from experts. Common roles of CHWs include health education, community mobilization, and community-based services, particularly related to reproductive, maternal, neonatal, and child health. Some countries utilize CHWs for non-communicable diseases and other emerging health issues. To maximize the potential contribution of CHWs to achieving Universal Health Coverage, we recommend future research and policy focus on strengthening existing health systems to support the expansion of CHWs roles and better integrating of CHWs into national PHC systems. This is Paper 4 in the Series on Primary Health Care in South Asia, addressing areas that have the potential to revitalize health systems in South Asian countries. Funding: The authors received financial support from the Department of Health Systems Development, WHO South-East Asia Regional Office (WHO SEAR).

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)0.003
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.007
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 head0.114
GPT teacher head0.406
Teacher spread0.292 · 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.

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

Citations23
Published2024
Admission routes1
Has abstractyes

Explore more

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