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Record W3147553710 · doi:10.1024/1662-9647/a000262

Happiness Inequality Among a Sample of Iranian Older Population

2021· article· en· W3147553710 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeroPsych · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsHappinessConfidence intervalInequalityIndex (typography)Odds ratioLogistic regressionDemographyMultivariate statisticsMultivariate analysisPopulationSample (material)PsychologyGerontologyMedicineStatisticsMathematicsSociologySocial psychology

Abstract

fetched live from OpenAlex

Abstract. This study evaluates the happiness inequality among older Iranians using concentration index analysis. A total of 739 people aged 60–90 years completed the Oxford Happiness Inventory (OHI) questionnaire. The SES variables were constructed using nonlinear principal component analysis (NLPCA) based on all related variables. The multivariate logistic regression analysis showed that persons in the SES quintiles 3–4, urban dwellers, literate, and with no underlying disease had higher odds of happiness than others. Based on the estimated concentration indices, there was inequality in happiness based on SES levels (concentration index [95% confidence Interval]: 0.14 [0.10, 0.19]; p < .05). Our results revealed that happiness in the older population was probably more prevalent among people with higher SES levels.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.341
Teacher spread0.307 · 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