Coaching for workers with chronic illness: Evaluating an intervention.
Bibliographic record
Abstract
Working with chronic illness may present challenges for individuals-for instance, managing symptoms at work, attaining accommodations, and career planning while considering health limitations. These challenges may be stressful and lead to strains. We tested a 12-week, 6-session, phone-based coaching intervention designed to help workers manage these challenges and reduce strains. Using theories of stress and resources, we proposed that coaching would help boost workers' internal resources and would lead to improved work ability perceptions, exhaustion and disengagement burnout, job self-efficacy, core self-evaluations, resilience, mental resources, and job satisfaction, and that these beneficial effects would be stable 12 weeks after coaching ended. Fifty-nine full-time workers with chronic illnesses were randomly assigned to either a coaching group or a waitlisted control group. Participants completed online surveys at enrollment, at the start of coaching, after coaching ended, and 12 weeks postcoaching. Compared with the control group, the coaching group showed significantly improved work ability perceptions, exhaustion burnout, core self-evaluations, and resilience-yet no significant improvements were found for job self-efficacy, disengagement burnout, or job satisfaction. Indirect effects of coaching on work ability, exhaustion and disengagement burnout, and job satisfaction were observed through job self-efficacy, core self-evaluations, resilience, and mental resources. Results indicated that the positive effects of coaching were stable 12 weeks after coaching ended. Results suggest that this coaching intervention was helpful in improving the personal well-being of individuals navigating challenges associated with working and managing chronic illness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".