Quality of Life in Personal Social Ecosystems: Further Psychometric Evaluation and Hungarian Adaptation of the Experience in Personal Social Systems Questionnaire
Bibliographic record
Abstract
Introduction: Hunger et al. (2014, 2015, 2017) developed the Experience in Personal Social Systems Questionnaire (EXIS.pers) to assess individuals’ perceived functioning in their personal ecosystems. Aims: The present study aims to 1) provide further data regarding this instrument’s psychometric characteristics that have not yet been investigated, as well as 2) describe the scale’s Hungarian adaptation. Methods: The present data set consisted of 400 questionnaires of 182 individuals recruited from the general population (83.8% female, Mage = 39.8 years, SDage = 9.3 years) participating in repeated assessments. The Brief Symptom Inventory, the SCOFF screening test, the Patient Health Questionnaire-15, the Meaning in Life Questionnaire, and the WHO Well-being Index were used to investigate construct validity. Results: A bifactor structure of the EXIS.pers fitted the data best according to the confirmatory factor analytic models. The results confirmed the scalar invariance of the best fitting bifactor model across both sex and time. Internal consistency of both the subscale and total scores was good according to both traditional (Cronbach’s alpha) and more advanced (omega) indicators. Test-retest reliability with one- and five-month time lag was appropriate, as well. EXIS.pers scores showed significant inverse association with all 13 indicators of psychopathology and positive associations with both indicators of positive mental health suggestive of appropriate validity. Conclusions: The results indicate that the EXIS.pers can be used with confidence when comparing men and women or in studies involving repeated-measures designs, and that the Hungarian version serves as a reliable and valid adaptation of the original instrument.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".