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Record W2949668127 · doi:10.5539/gjhs.v11n7p119

The Health Promotion Model of Public Health Program for Elderly

2019· article· en· W2949668127 on OpenAlexvenueno aff
Arita Murwani, Santoso Santoso, Eny Lestari, Endang Sutisna Sulaeman

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionPublic healthIndependence (probability theory)Health educationHealth carePromotion (chess)GerontologyElderly peopleHealth policyMedicineEnvironmental healthNursingEconomic growthPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The society health care of elderly is integral part of service health by comprehensive through promotion, preventive, curative and rehabilitative, and resocialitative efforts. The aim of society health care is to improve the ability of society to live healthy until an optimal degree of health is achieved. METHOD: This research was cross sectional research by using survey method. Sample of this research was 200 elderly that was divided into 25 clinics in Sleman regency of Special Region Yogyakarta. This research was done on March up to August 2018. The data was collected then processed by using PLS SEM program. RESUTLS: The results of research show there is an influence between the health promotion and the health education with estimates = 0,753. The health education posses the elderly health behavior with value p = 0,00. The health behavior (p = 0,00), public policy (p = 0,07), the care function of elderly (p = 0,00), and elderly behavior (p = 0,020) posse the independence of elderly. The elderly independence possess the elderly health quality with estimates as big as 0, 312. CONCLUSION: Based on the finding of the study, elderly health quality can be improved by increasing the elderly independence through the health education effort which takes effect to the health behavior and improving the facilities and infrastructure related to the health public policy, and improving the health care of society.

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)0.015
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.096
GPT teacher head0.429
Teacher spread0.333 · 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 designOther design
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

Citations2
Published2019
Admission routes1
Has abstractyes

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