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Record W2314575957 · doi:10.4992/jjpsy.85.69

The Hedonic and Eudaimonic Motives for Activities (HEMA) in Japan: The pursuit of well-being

2014· article· en· W2314575957 on OpenAlexaff
Ryosuke Asano, Tasuku Igarashi, Saori Tsukamoto

Bibliographic record

VenueThe Japanese journal of psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPleasurePsychologyEudaimoniaScale (ratio)Social psychologyWell-beingLife satisfactionPositive psychologySubjective well-beingHappinessPsychotherapist

Abstract

fetched live from OpenAlex

Hedonia (seeking pleasure and relaxation) and eudaimonia (seeking to improve oneself in congruence with one's values) uniquely contribute to well-being. The authors developed and tested the construct validity of a Japanese version of the Hedonic and Eudaimonic Motives for Activities (HEMA) scale that had been originally developed in North America. Drawing on the theoretical and empirical evidence from research on emotion, we proposed that people would pursue well-being in three different directions: pleasure, relaxation, and eudaimonia. In Study 1, we used the original HEMA scale to examine the Japanese attainment of well-being. The results supported the hypothesized three-factor model. Study 2 revealed that the Japanese version of the HEMA scale measured pleasure, relaxation, and eudaimonia. Each of these subscales showed statistically sufficient internal consistency. There was no gender difference in any of these measures. Scores on the scale systematically corresponded with external criterion variables, such as life satisfaction, affect, Ryff's psychological well-being, social support, and lifestyle. Implications for psychological research and public policies that cover the topic of the pursuit of well-being are discussed.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.329
Teacher spread0.312 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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