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Record W2615981514 · doi:10.5539/res.v9n2p275

Evaluation of Relationship between Social Support and Social Health of Tehran Citizens

2017· article· en· W2615981514 on OpenAlexvenueno aff
Habib Sabouri Khosro Shahi, Somayeh Mashayekhi

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSocial supportPsychologySocial psychologyProsperityData collectionCluster samplingCohesion (chemistry)SociologySocial sciencePolitical scienceDemography

Abstract

fetched live from OpenAlex

Social health is a concept that is created from relationship between two concepts of health and society. Since the society is a nominal concept and its external truth depends on everyone who has formed it, in evaluation of society, people of that should be evaluated and studied more than everything. In this regard the present research under the topic of “Evaluation of relationship between social support and social health of Tehran citizens” has been conducted among Tehran citizens in 2016. Social health was evaluated based on Keyes’s theories in five dimensions of social prosperity, social adaptation, social cohesion, social acceptance and social participation and also social support was evaluated based on researches of Wax et al. in three dimensions of friends support, family support and others support. Research methodology in this research is survey and technique of data collection is questionnaire. The questionnaires were distributed among 400 people of women who were selected as multi-stage cluster sampling, and because 15 of questionnaires were altered, approximately 385 were finally analyzed. The result was that social support is one of effective factors on social health; the obtained adjusted multiple coefficient of determination showed that the triple dimensions of social support (friends support, family support and others support) have been able to express approximately 58% of the social health variable.

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)0.007
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.349
GPT teacher head0.523
Teacher spread0.175 · 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
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
Published2017
Admission routes1
Has abstractyes

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