Hedonic vs. Eudaimonic Ways of Living on the Path to Well-Being and Psychological Distress: Turkish Validation of HEMA-R
Bibliographic record
Abstract
The Hedonic and Eudaimonic Motives for Activities-Revised (HEMA-R) measures eudaimonic, hedonic, hedonic pleasure, and hedonic comfort motivations. We tested the psychometric properties of the HEMA-R among Turkish-speaking university students (N = 255) and adults (N = 460). Confirmatory factor analyses among university students demonstrated both two-factor and three-factor solutions of the HEMA-R, while confirmatory factor analyses among adults identified a three-factor solution. Internal consistencies of the HEMA-R were largely over α and ω > .80. In both samples, eudaimonic motivation always had at least slightly more positive associations with well-being indicators compared to hedonic motivation, hedonic pleasure motivation, and hedonic comfort motivation, while having negative weak relationships in half of the analyses with ill-being indicators. Hedonic motivation had mostly weak positive associations with the majority of well-being outcomes, while surprisingly having weak positive associations with several indices of ill-being. Hedonic pleasure motivation had weak positive associations with the majority of well-being indicators, and hedonic comfort motivation did not have any association with some of the well-being indicators. They predominantly had no associations with ill-being indicators. Eudaimonic and hedonic indicators of motivation both related to need satisfaction and meaning in life indicators. Implications are discussed for future research.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".