SPECIAL ISSUE: Differentiating Successful from Less Successful Males and Females in a Group Relaxation/Biofeedback Stress Management Program
Bibliographic record
Abstract
Managing stress in the post-COVID world requires a program that can efficaciously and cost-effectively address a large number of people who have differing experiences and needs and can also be adapted for internet presentation. The purpose of this paper is to share observations, collected over more than forty years, of group stress management training in university and community settings. The specific data reported are from a subgroup that is representative of the other groups. An in-person group stress-management program of 141 adults in community clinics with approximately 15 to 20 per group attended 10 training sessions across 5 weeks with pre-post personality measures. The group also documented 5 weeks of home practice, symptoms, and medication use. The average group improvement in well-being was 80%, following training in breathing, progressive muscle relaxation, autogenic training, visualization, quieting response, and alphagenics, with individual temperature biofeedback having been provided during the last 5 classes. A pre- to post-two-way analysis of variance (ANOVA) revealed that the Spielberger State-Trait Anxiety Inventory (STAI) trait anxiety and Eysenck Personality Inventory (EPI) neuroticism significantly decreased. EPI extraversion increased only in females. Males and females equally preferred autogenic training (55%). The most successful males and females were older, practiced more, reported greater increases in self-confidence, and attributed more of their success to the group and/or instructor. The success of a program may also be associated with excellent home practice compliance, being a part of a group, and increases in self-confidence/efficacy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".