Mindfulness-based Practices in Workers to Address Mental Health Conditions: A Systematic Review
Bibliographic record
Abstract
The effectiveness of mindfulness techniques in addressing address mental health conditions in workers is uncertain. However, it could represent a therapeutic tool for workers presenting with such conditions. Our objective was to assess the effects of mindfulness-based practices for workers diagnosed with mental health conditions. We conducted a systematic review of randomized controlled trials (RCT). Participants included were workers with a mental health condition. Interventions included any mindfulness technique, compared to any non-mindfulness interventions. Outcomes were scores on validated psychiatric rating scales. 4,407 records were screened; 202 were included for full-text analysis; 2 studies were included. The first study (Finnes et al, 2017) used Acceptance and Commitment Therapy (ACT) associated or not with Workplace Dialogue Intervention (WDI), compared to treatment as usual. At 9 months follow-up, for the ACT group, depression scores improved marginally (Standardized Mean Difference, SMD: -0.06, p=0.021), but anxiety scores were worse (SMD: 0.15, p=0.036). Changes in mental health outcomes were not statistically significant for the ACT+WDI group. In the second study (Grensman et al, 2018), no statistically significant change in mental health scales has been observed after completion of mindfulness-based cognitive therapy (MBCT) compared to Cognitive-Behavioral Therapy (CBT). Substantial heterogeneity precluded meta-analysis. This systematic review did not find evidence that mindfulness-based practices provide a durable and substantial improvement of mental health outcomes in workers diagnosed with mental health conditions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".