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Record W4415544163 · doi:10.1016/j.lana.2025.101236

Community-driven strategies for primary health care resilience in response to shocks in Latin America and the Caribbean: a scoping review and expert consultation

2025· article· en· W4415544163 on OpenAlexaff
Natalia Houghton, Ernesto Báscolo, Claudia Zavaleta, Walter Flores, Myrna Cunningham Kain, Carina Isabel Vance Mafla, Jeannie Haggerty

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcGill University
FundersPan American Health Organization
KeywordsLatin AmericansPsychological interventionPreparednessResilience (materials science)Work (physics)Health carePsychological resiliencePrimary health care

Abstract

fetched live from OpenAlex

Community engagement in Primary Health Care (PHC) enhances system resilience. This scoping review and expert consultation aimed to document the range of community-driven interventions and strategies implemented in Latin America and the Caribbean (LAC) in response to shocks and identify factors that enable or hinder their implementation. The research used a mixed-methods approach, including a scoping review of 70 studies from January 2019 to July 2024 and interviews with seven subject experts. The findings were then validated with six additional experts. The study identified 14 community-driven strategies, grouped into three main categories: community health worker participation, community engagement, and mobilization of civil society organizations. For each category, the researchers analyzed facilitators and challenges. This work provides a comprehensive compilation and analysis of community-driven interventions during emergencies in LAC. The findings offer valuable insights for improving emergency preparedness and response strategies in health systems.

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: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.088
GPT teacher head0.385
Teacher spread0.297 · 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 designObservational
Domainnot available
GenreCommentary

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

Citations1
Published2025
Admission routes1
Has abstractyes

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