Complex interactive multimodal intervention to improve personalized stress management among healthcare workers in China: A knowledge translation protocol
Bibliographic record
Abstract
Objectives: Numerous stress management interventions have been implemented in the workplace, but few are adapted to the healthcare setting. Due to the nature of their jobs, healthcare workers (HCWs) may find it difficult to adopt recommended stress management strategies. We present the protocol for a 12-week personalized stress management intervention among HCWs to change their behavior as well as improve physiological/psychological outcomes. Methods: It is a pragmatic quasi-experimental study involving stressed HCWs from two general hospitals in Wuhan, China. The intervention group will receive a complex interactive multimodal intervention, including advanced education via mobile connection, participation in a web-based social network, tailored feedback, and the support of a nurse coach, while the control group will engage in self-guided stress management. Results: The primary outcome is centered on behavioral measures, namely improvements in stress management practice frequency after a 12-week intervention. The secondary outcomes are the changes in stress-related physiological indices (i.e. high frequency variability and normalized unit assessed by Holter) and psychological indicators (scores on the Perceived Stress Scale and Depression, Anxiety, Stress Scale) following 12 weeks of treatment. Conclusion: The knowledge translation intervention builds on a body of work defining the role of individualized instruction and feedback intervention, as well as group intervention through WeChat social network and personalized coaching. We believe this novel intervention will help HCWs promote their stress management awareness and skills, and ultimately benefit their long-term health. Trial Registration: ClinicalTrials.gov., NCT05239065. Registered 14 February 2022-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05239065.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".