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Record W3191169659 · doi:10.22605/rrh6774

Challenges in the sociocultural milieu of South Asia: a systematic review of community health workers

2021· review· en· W3191169659 on OpenAlexaff
Umair Majid, ‎ Zahid, Harold, Zain, Sood

Bibliographic record

VenueRural and Remote Health · 2021
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcMaster UniversityUniversity of TorontoInstitute for Work & HealthWestern University
Fundersnot available
KeywordsSociocultural evolutionHealth careSociologyPublic relationsMedicineEconomic growthPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

INTRODUCTION: Community health workers (CHWs) connect patients in rural and remote communities to health service organizations. This diverse group of healthcare workers has helped improve healthcare access and outcomes and enhance the quality of life for people in hard-to-reach communities. However, CHWs face numerous challenges rooted in the sociocultural milieu of the region and country in which they reside. METHODS: This systematic review and qualitative meta-synthesis of 38 studies examines the sociocultural challenges that CHWs experience; it focuses on the unique history, geography, and sociocultural milieu of South Asia. RESULTS: This study found the following challenges that CHWs regularly face when working in communities: religious and cultural norms and practices, gender and biological sex, caste, and generation. All challenges in some way relate to one another and stem from the unique sociocultural milieu of South Asia, and the various subcultures that exist in this diverse region. CONCLUSION: This article presents important guidance for program planning and CHW deployment that reflects the sociocultural realities of practice. The findings of this investigation may serve as an essential resource for program planners and decision-makers in improving the effectiveness and reach of CHW programs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.220
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.099
GPT teacher head0.389
Teacher spread0.289 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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