The Impact of HeartMath Resiliency Training on Health Care Providers
Bibliographic record
Abstract
BACKGROUND: Health care providers must think clearly and make critical decisions under stressful circumstances. Providing effective strategies for managing stress in the moment helps mitigate the physical, emotional, and psychological impacts associated with caring for others and promotes resiliency. Staff may also utilize these techniques with patients and their families to help alleviate the symptoms of stress that may be experienced as the result of illness. AIM: The purpose of this study was to measure whether HeartMath techniques reduce stress and improve resiliency in health care providers. METHODS: Study participants were asked to complete the Personal and Organizational Quality Assessment-Revised 4 Scale (2016) immediately before the start of training and then again 4 to 6 weeks after completion of the class. Participants were also asked to voluntarily share their experiences using HeartMath techniques personally or with family, friends, and patients. RESULTS: Significant improvements were found in 3 of 4 primary scales (organizational stress, emotional stress, and physical stress) and in 6 of 9 subscales on the Personal and Organizational Quality Assessment-Revised 4 Scale indicating a positive impact on employee health, well-being, and performance. Stories shared by participants posttraining indicated that HeartMath techniques were being used personally and with patients as an adjunct in the management of pain, anxiety, and insomnia. CONCLUSIONS: This study supports existing evidence that HeartMath techniques are effective in managing stress and increasing resiliency. These techniques are also valuable tools for health care providers to use with patients and their families in the management of symptoms such as pain, anxiety, and sleeplessness related to hospitalization and illness.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".