MétaCan
Menu
← Back to cohort
Record W3177420936 · doi:10.5539/gjhs.v13n8p42

Rural Community Health in Nicaragua: Needs Assessment Results and Self-Reported Health Status

2021· article· en· W3177420936 on OpenAlexvenueno aff
Jennifer Harsh Caspari, Matthew Martin, Karen Herrera

Bibliographic record

VenueGlobal Journal of Health Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthMedicineDiseaseCommunity healthHealth equityRural healthGerontologyLatin AmericansRural areaRural communityHealth carePublic healthSocioeconomicsNursingEconomic growth

Abstract

fetched live from OpenAlex

Chronic disease-related burden and deaths are increasingly worldwide. The Latin American country of Nicaragua is no exception. Cardiovascular disease (CVD) is the leading cause of death in Nicaragua and health behaviors, such as sedentary lifestyle and tobacco use, can exacerbate risk associated with CVD. The aims of this study were to identify risk factors and health-promoting behaviors associated with CVD present in three rural communities in Nicaragua. Results from a needs assessment indicated that need assessments that utilize patient-reported outcome measures were feasible to conduct in rural Nicaragua communities, typical CVD risk factors were not prevalent in the rural communities we surveyed, and the overall good health of these communities reflects the perceived health status reported by participants. Findings of this study suggest that community health workers could consider hypertension and diabetes treatment, patient health information on nutrition, healthcare access, and healthy food access as potential target areas to improve community health in Nicaragua.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.396
Teacher spread0.346 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health Science→Same topicGlobal Public Health Policies and Epidemiology→French-language works237,207→