Quantifying Insufficient Coping Behavior under Chronic Stress: A Cross-Cultural Study of 1,303 Students from Italy, Spain and Argentina
Bibliographic record
Abstract
The question of how to quantify insufficient coping behavior under chronic stress is of major clinical relevance. In fact, chronic stress increasingly dominates modern work conditions and can affect nearly every system of the human body, as suggested by physical, cognitive, affective and behavioral symptoms. Since freshmen students experience constantly high levels of stress due to tight schedules and frequent examinations, we carried out a 3-center study of 1,303 students from Italy, Spain and Argentina in order to develop socioculturally independent means for quantifying coping behavior. The data analysis relied on 2 self-report questionnaires: the Coping Strategies Inventory (COPE) for the assessment of coping behavior and the Zurich Health Questionnaire which assesses consumption behavior and general health dimensions. A neural network approach was used to determine the structural properties inherent in the COPE instrument. Our analyses revealed 2 highly stable, socioculturally independent scales that reflected basic coping behavior in terms of the personality traits activity-passivity and defeatism-resilience. This replicated previous results based on Swiss and US-American data. The percentage of students exhibiting insufficient coping behavior was very similar across the study sites (11.5-18.0%). Given their stability and validity, the newly developed scales enable the quantification of basic coping behavior in a cost-efficient and reliable way, thus clearing the way for the early detection of subjects with insufficient coping skills under chronic stress who may be at risk of physical or mental health problems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".