Developing a prognostic model for stress reduction in patients with prolonged work‐related stress
Bibliographic record
Abstract
Mindfulness-based stress reduction (MBSR) is a 9-session group-treatment programme for managing stress. Research suggests variability in the outcomes of MBSR among participants. This prognostic (not causal) study develops a multivariable model that may support clinicians in forecasting expected MBSR outcomes. We used data of 763 patients collected from MBSR programs conducted between October 2015 and March 2022. Candidate prognostic factors at baseline included psychosocial work environment, sociodemographic, and clinical information. Multiple imputation was used to handle missing data (imputations = 200). Important prognostic factors were backward selected in ≥5% of the imputed datasets. The final prediction model including the selected prognostic factors was evaluated using linear regression with a four-fold internal cross-validation procedure. Reductions in perceived stress from baseline to end of the MBSR programme were predicted by a lower General Severity Index (β = 2.00, p < 0.01), higher baseline levels of stress (β = -0.88, p < 0.01), and somewhat by having managerial responsibility in the latest job (vs. no; β = -2.53, p = 0.07). The remaining prognostic factors were weaker predictors, for example, gender and income. Internal validity of the final model was indicated by consistent results from four randomly folded subsamples. This study developed a prognostic model predicting changes in stress levels in relation to the MBSR programme. A reduction in stress level was particularly predicted by milder psychological symptoms and higher baseline levels of perceived stress. These predictions cannot be taken as evidence of causal associations. Forecasting of the illness course should be cautiously practiced using clinical judgement regarding individual patients.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".