Coping strategies patterns to buffer the psychological impact of the State of Emergency in Spain during the COVID-19 pandemic’s early months
Bibliographic record
Abstract
Coping style represents the cognitive and behavioral patterns to manage particular demands appraised as taxing the resources of individuals. Studies report associations between certain coping styles and levels of adjustment of anxious symptomatology and emotional distress. The main objective of this study was to analyze behavioral co-occurrent patterns and relationships in the coping strategies used to deal with psychological distress displayed by the Spanish adult population during the first State of Emergency and lockdown of the COVID-19 pandemic. This is a cross-sectional study that uses selective methodology complemented with an indirect observational methodology, with a nomothetic/punctual/unidimensional design. We collected 996 surveys from 19 out of the 22 autonomous regions in Spain. We focused the analysis on sociodemographic variables, cumulative incidence of the COVID-19 disease and psychological distress variables. We performed two different inferential analyses: Lag sequential analysis to define the participant coping patterns, and polar coordinate analysis to study the interrelationship of the focal behavior with conditioned behaviors. We found behavioral co-occurrent patterns of coping strategies with problem avoidance being found as the coping strategy most frequently engaged by participants. Interestingly, the problem avoidance strategy was not associated with lower anxious symptomatology. By contrast, emotion-focused strategies such as express emotions and social support were associated with higher anxious symptomatology. Our findings underscore the importance of furthering our understanding of coping as a way to aid psychological distress during global public health emergencies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".