Bibliographic record
Abstract
My keynote talk is based on the findings from our recent systematic review regarding self-compassion interventions for workplace wellbeing outcomes. This paper is currently accepted with minor revisions in a leading psychology journal. Self-compassion has been reported as highly relevant to wellbeing outcomes in many different populations, including employees. The importance of self-compassion in the workplaces has been increased during the COVID-19 pandemic; employees need to care for themselves in order to maintain good wellbeing, leading to long-lasting high performance. However, empirical evidence for self-compassion interventions in occupational settings has not been systematically reviewed to date. Accordingly, the primary purposes of this systematic review were to 1) synthesize and evaluate the efficacy of self-compassion interventions for employee wellbeing, and 2) assess the methodological quality of relevant studies. Research databases such as ProQuest, PsycINFO, Science Direct, and Google Scholar were used to identify relevant studies. The Newcastle-Ottawa Scale was usd to evaluate the quality of non-randomized trials, and the Quality Assessment Table was used to assess the quality of randomized controlled trials. 3,387 articles were originally retrieved, from which ten studies met all the inclusion criteria. All of the ten studies reported positive impacts of self-compassion interventions for employee wellbeing. The quality of reserach methods was medium. The participants of all ten studies were in a caring profession, and most of the studies were conducted in Western countries. The Self-Compassion Scale (SCS) or its short-form was used in almost all assessments. Findings suggest that self-compassion interventions can cultivate self-compassion and enhance other employee wellbeing outcomes in worker populations. However, overall, the quality of research methods needs to be improved in order to further appraise the applications and limitations of this approach in occupational contexts. Moreover, future studies should recruit a wider range of employee samples, including non-caring professions as well as employees working in non-Western 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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".