A rank ordering and analysis of four cognitive-behavioral stress-management competencies suggests that proactive stress management is especially valuable
Bibliographic record
Abstract
The main objective of this study was to determine the relative value of four cognitive-behavioral competencies that have been shown in empirical studies to be associated with effective stress management. Based on a review of relevant psychological literature, we named the competencies as follows: Manages or Reduces Sources of Stress, Manages Thoughts, Plans and Prevents, and Practices Relaxation Techniques. We measured their relative value by examining data obtained from a diverse convenience sample of 18,895 English-speaking participants in 125 countries (65.0% from the U.S. and Canada) who completed a new inventory of stress-management competencies. We assessed their relative value by employing a concurrent study design, which also allowed us to assess the validity of the new instrument. Regression analyses were used to rank order the four competencies according to how well they predicted desirable outcomes. Both regression and factor analyses pointed to the importance of proactive stress-management practices over reactive methods, but we note that the correlational design of our study has no implications for the possible causal effects of these methods. Questionnaire scores were strongly associated with self-reported happiness and also significantly associated with personal success, professional success, and general level of stress. Data were collected between 2007 and 2022, but we found no effect for time. The study supports the value of stress-management training, and it also suggests that moderate levels of stress may not be as beneficial as previously thought.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".