Tai Chi and workplace wellness for health care workers: a systematic review
Bibliographic record
Abstract
Abstract Background Several studies show positive effects of new non-medical therapies known as complementary and alternative medicines, such as the discipline of tai chi. As healthcare professions are among the most vulnerable for work-related stress, this systematic review aims to investigate the relationship between tai chi practice and wellness of healthcare workers. Methods Cinahl, Scopus, Web of Science and PubMed were searched in September 2019. Full-text articles, written in English and published after 1995, were recruited if they focused on positive effects of tai chi on the psychophysical wellbeing of healthcare workers, in comparison with alternative techniques (such as yoga or traditional care). Outcomes were reduced work-related stress, better physical and psychological function, improvement in attention and/or productivity; no restrictions about study design were applied. Quality assessment was performed with the Newcastle-Ottawa Scale on cohort/cross-sectional studies, the Jadad scale for randomized clinical trial, AMSTAR for systematic reviews and CASE REPORT scale for case study. Results 6/111 papers were included: 3 clinical trials, 1 observational study, 1 systematic review and 1 case report. The methodological quality was of medium level. 2/3 trials found a significant increase in individuals' wellbeing and improvements in stress levels and nursing staff’s motivation in their work. In the observational study tai chi was a prevalent mind-body practice to reduce stress. The systematic review suggested that tai chi could be a useful tool to reduce stress-related chronic pain. In case report the effectiveness was observed in medical students. Conclusions This study highlights the full potential and possible benefits derived from tai chi but its application to improve health professionals' wellbeing is still limited, and the absence of a standardized intervention impacts on the methodological quality and reduces the robustness of the retrieved evidence. Key messages Tai chi can improve many pathological conditions and reduce work-related stress. Further research is needed to gain robust evidence of its efficacy for wellbeing of healthcare workers.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".