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Record W2209041855 · doi:10.5539/gjhs.v8n8p94

An Analysis of the Structural Factors Affecting the Public Participation in Health Promotion

2015· article· en· W2209041855 on OpenAlexvenueno aff
Raheleh Ghaumi, Tayebe Aminee, Akram Aminaee, Mojgan Dastoury

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Health promotionPublic healthStructural equation modelingEnvironmental healthPsychologyMedicinePolitical scienceNursingComputer sciencePolitics

Abstract

fetched live from OpenAlex

The present study focuses on analyzing national and international Community-Based Participatory Research (CBPR) studies published from 2000 to 2010 in order to identify and categorize the possible factors that affect social participation for improving the public health. Clearly, improving the public health necessitates a combination of the participation and responsibility by the social members and the attempts by public health policy-makers and planners. CBPR studies are selected as the corpus since they seek to encourage active and informed participation of the social members in fulfilling the health related goals. The present study is conducted through meta-synthesis within a qualitative framework. The results revealed a set of factors within the structural capacities which were employed by the CBPR researchers for achieving the health promotion goals. The structural capacities employed in the interventions could be considered on the cultural and social grounds. The cultural grounds were divided into scientific and religious attempts. For the scientific attempts, the results highlighted the participation of higher education institutes including universities and research centers as well as educational institutes such as schools and the relevant institutions. And regarding the religious attempts, the results indicated that the cooptation of religious centers played the greatest role in enhancing the public participation.

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)0.026
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.186
GPT teacher head0.558
Teacher spread0.373 · 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.

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
Published2015
Admission routes1
Has abstractyes

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