Tai Chi and Workplace Wellness for Health Care Workers: A Systematic Review
Bibliographic record
Abstract
Several studies show the positive effects of new non-medical therapies known as complementary and alternative medicines (CAMs). In this context, the discipline of tai chi is obtaining a wider consensus because of its many beneficial effects both on the human body and mind. The aim of this study was to perform a systematic review of the scientific literature concerning the relationship between tai chi practice and wellness of health care workers (HCW) in their professional setting. The research was performed in September 2019, investigating the databases Cinahl, Scopus, Web of Science, and PubMed. Full-text articles, written in English language and published after 1995, were taken into account. No restrictions regarding the study design were applied. A quality assessment was developed using AMSTAR, Jadad, Newcastle-Ottawa Scale, INSA, and CASE REPORT scale. Six papers were finally included: Three clinical trials, one observational study, one systematic review, and one case report. The methodological quality of the included studies was judged as medium level. In conclusion, this systematic review suggests the potential impact of interventions such as tai chi as tools for reducing work-related stress among healthcare professionals. Further research will be needed in order to gain robust evidence of its efficacy.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".