A Smartphone App to Reduce Burnout in the Emergency Department: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: Burnout is a significant concern among health care professionals, particularly those working in the emergency department (ED). Given the negative personal and professional consequences that burnout can have on all health care professionals, multidisciplinary solutions are needed to address burnout. Our objective was to evaluate the feasibility and potential impact of resilience training delivered through a smartphone application on burnout among health care professionals working at a tertiary-care pediatric ED. Methods: We conducted a single-center pilot randomized controlled study enrolling multidisciplinary health care professionals working in our ED. Participants assigned to the intervention group received self-driven access to a smartphone application that provided a structured resilience curriculum for a period of 3 months. The participants completed psychometric assessments both prior to and following the invention period. Changes in psychometric measures of the intervention group were then compared with a waitlist-control group. Results: Following the intervention period, a total of 20 participants were included in the final analysis. The change in participant scores on psychometric measures prior to and following the intervention period was calculated. A statistically significant mean decrease in burnout measure (emotional exhaustion subscale of Maslach-Burnout Inventory mean score −5.88, p < .001) and increase in mindfulness measure (Mindful Attention Awareness Scale mean score 0.51, p < .001) was observed among the intervention group participants. Conclusions/Application to Practice: Our study suggests that a resilience training program delivered using a smartphone application can be an effective intervention in reducing burnout and increasing mindfulness skills. Our study also demonstrated the potential feasibility of a randomized controlled study of burnout within a multidisciplinary group of health care professionals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".