Adaptation and implementation of an evidence-based behavioral medicine program in diverse global settings: The Williams LifeSkills experience
Bibliographic record
Abstract
Epidemiological research has documented the health-damaging effects of psychosocial factors like hostility, depression, anxiety, job stress, social isolation and low socioeconomic status. Several studies suggest that behavioral interventions can reduce levels of these psychosocial factors. Herein we describe the translational process whereby the Williams LifeSkills® (WLS(®)) program and products for reducing psychosocial risk factors have been developed and tested in clinical trials in the U.S. and Canada and then adapted for other cultures and tested in clinical trials in other countries around the world. Evidence from published controlled and observational trials of WLS(®) products in the U.S. and elsewhere shows that persons receiving coping skills training using WLS(®) products have consistently reported reduced levels of psychosocial risk factors. In two controlled trials, one for caregivers of a relative with Alzheimer's Disease in the U.S. and one for coronary bypass surgery patients in Singapore, WLS(®) training also produced clinically significant blood pressure reductions. In conclusion, WLS(®) products have been shown in controlled and observational trials to produce reduced levels of both psychosocial and cardiovascular stress indices. Ongoing research has the potential to show that WLS(®) products can be an effective vehicle for the delivery of stress reduction and mental health services in developing countries.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".