Effects of Self-Compassion Training on Work-Related Well-Being: A Systematic Review
Bibliographic record
Abstract
Self-compassion, sharing some commonalities with positive psychology 2.0 approaches, is associated with better mental health outcomes in diverse populations, including workers. Due to the COVID-19 pandemic, there is heightened awareness of the importance of self-care for fostering mental health at work. However, evidence regarding the applications of self-compassion interventions in work-related contexts has not been systematically reviewed to date. Therefore, this systematic review aimed to synthesize and evaluate the utility of self-compassion interventions targeting work-related well-being, as well as assess the methodological quality of relevant studies. Eligible articles were identified from research databases including ProQuest, PsycINFO, Science Direct, and Google Scholar. The quality of non-randomized trials and randomized controlled trials was assessed using the Newcastle-Ottawa Scale and the Quality Assessment Table, respectively. The literature search yielded 3,387 titles from which ten studies met the inclusion criteria. All ten studies reported promising effects of self-compassion training for work-related well-being. The methodological quality of these studies was medium. All ten studies recruited workers in a caring field and were mostly conducted in Western countries. The Self-Compassion Scale or its short-form was used in almost all instances. Findings indicate that self-compassion training can improve self-compassion and other work-related well-being outcomes in working populations. However, in general, there is need for greater methodological quality in work-related self-compassion intervention studies to advance understanding regarding the applications and limitations of this technique in work contexts. Furthermore, future studies should focus on a broader range of employee groups, including non-caring professions as well as individuals working in non-Western countries.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".