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Record W2554522665 · doi:10.5539/gjhs.v9n6p76

A Contribution of PAR for the Prevention and Control of Hypertension and Diabetes among the Elderly in Thailand

2016· article· en· W2554522665 on OpenAlexvenueno aff
Siripun Bootsri, Kasam Nakornkate, Suchitra Sukonthasab

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionParticipatory action researchMedicineCitizen journalismPromotion (chess)Community healthGerontologyPublic healthNursingHealth educationIntervention (counseling)Political scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: A rise in the number of elderly people in society increases the need for disease prevention and health promotion services as the growing figure entails more patients suffering from chronic illnesses, such as hypertension and diabetes. In order to prevent and control these non-communicable diseases, Participation Action Research (PAR) can be a powerful mechanism for engaging the elderly in PAR, and has the potential to provide a way to enhance the health of the elderly, consequently; it is necessary to design an appropriate health promotion model involving community participation.OBJECTIVE: This participation action research aims to develop a health promotion model integrating community participation for the prevention and control of hypertension and diabetes in the elderly and to implement a local intervention.METHODS: Mixed methodologies of mainly a qualitative approach and a supported quantitative approach with a questionnaire were employed. This was community-based participatory action research in which the researchers and community partners were the main participants. The PAR model was developed interactively in collaboration with the setting of local administration, the public sector, and the private sector. The process involved assessing the situation, taking action to promote community participation based on the analysis, implementing the solution, and testing the model and evaluating the model applying the After Action Review (AAR) approach.RESULTS: A health promotion model was developed and then piloted by the community team. The model is designed to improve the health behavior of the elderly; preventing and controlling hypertension and diabetes; providing continuous health education, especially regarding the importance of nutrition; physical activity; stress management; and facilitating the access of the vulnerable elderly to health services. The results showed that all the sectors, namely, families, schools, and temples, were involved in every stage of the research. The research results indicate that the model can build community health-promotion capacity, partnership development, community health plans, and community innovation and build a supportive environment.CONCLUSION: The paper illustrates that PAR has the potential to provide a way to make the significant role of community participation in a project for the prevention and control of hypertension and diabetes and for the promotion of healthy aging in a rural setting. Moreover, PAR can enhance program design and implementation based on the sharing of best practices and the active engagement of community members. In this way, the elderly can perceive benefits of group participation in enhancing their self-care ability, their sense of empowerment, and their ability to learn to prevent, control and sustain changes in their health behavior.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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