Reducing Anxiety and Improving Engagement in Health Care Providers Through an Auricular Acupuncture Intervention
Bibliographic record
Abstract
BACKGROUND: Stress and anxiety are experienced by health care providers as a consequence of caregiving and may result in physical, emotional, and psychological outcomes that negatively impact work engagement. AIM: The purpose of this study was to determine whether auricular acupuncture can reduce provider anxiety and improve work engagement. METHODS: Study participants received 5 auricular acupuncture sessions within a 16-week period utilizing the National Acupuncture Detoxification Association protocol for treating emotional trauma. Each participant completed the State-Trait Anxiety Inventory and the Utrecht Work Engagement Scale (UWES-9) prior to their first session and again after their fifth treatment. RESULTS: Significant reductions were found in state and trait anxiety (State-Trait Anxiety Inventory), as well as significant increases in the overall scores on the UWES as compared with baseline. Only the dedication subcategory of the UWES showed significant improvement. CONCLUSIONS: Engagement has been linked to increased productivity and well-being and improved patient and organizational outcomes. Providing effective strategies such as auricular acupuncture to support health care providers in reducing anxiety in the workplace may improve engagement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".