Investigating the Relationship Between Worry and Social Problem-Solving Attitudes and Performance
Bibliographic record
Abstract
The present study investigated the state-dependent, performance-based problem-solving abilities of individuals with chronic worry. A 2 (group: high trait worry vs. low trait worry) X 2 (induction type: worry vs. neutral verbal mentation) factorial design was employed to investigate the differential effects of state worry, and neutral mentation as a control condition, on performance-based problem-solving effectiveness. Independent samples t-tests tested for group differences in self-reported problem-solving attitudes. Secondary objectives involved investigating the relationship between problem-solving effectiveness and working memory and attentional control, emotional dysregulation, and abstraction in verbal worry. Contrary to predictions, there were no significant within (i.e., mentation style) or between group (i.e., worry severity) differences on objective problem-solving performance. Previous findings that individuals with chronic worry endorse greater tendencies to self-report unconstructive problem solving attitudes were replicated. Findings suggest that when employing problem-solving interventions with a high worry population, emphasis should be placed on changing maladaptive problem attitudes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".