Assessment of the environmental situation in the region as a moderator of the relationship between resilience and health-related quality of life in student youth
Bibliographic record
Abstract
Introduction. Poor environmental conditions in the region of residence affect the health-related quality of life and constitute a risk factor which is widely discussed in modern psychological research into the problems of youth resilience. The article presents the results of a study of the relationship between resilience and health-related quality of life in student youth living in regions with different environmental conditions. Materials and methods. The sample included 311 students of vocational schools and higher education institutions aged 16–20 from regions with different environmental conditions. Empirical data were collected by the Child and Youth Resilience Measure (CYRM-28), the 20-Item Short Form Health Survey (SF-20) and a questionnaire for assessing the environmental situation in the region of residence. Results. Young people who live in environmentally distressed areas showed lower scores when assessing environmental conditions (F = 20.60 at p < 0.001), as well as lower scores of resilience and its components (4.48 ≤ F ≤ 5.71 at p < 0.001), but they showed higher scores of physical functioning and no significant differences in other parameters of quality of life (F = 5.16 at p < 0.05), as compared to the youth from regions with good environmental situation. Modeling by structural equations with path analysis showed that resilience is directly influenced by indicators of current health perception, as well as indicators of physical functionating and role functioning (with opposite signs). The contribution of these indicators is mediated by the assessment of environmental conditions (χ2 = 2.98, CFI = 0.995, RMSCA = 0.049). Higher scores in assessment of environmental conditions enhance the positive effect of role functioning indicators and mitigate the negative effect of the physical functioning indicators on resilience. Conclusion. The results of the study can be used in the development of psychological support programs for young people living in environmentally distressed regions. Such programs should take into account the tendency of youth to overestimate the quality of physical functioning against the background of low assessment of environmental conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".