Online Modules to Alleviate Burnout and Related Symptoms Among Interdisciplinary Staff in Long-Term Care: A Pre-post Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The rising trend of providing palliative care to residents in Canadian long-term care facilities places additional demands on care staff, increasing their risk of burnout. Interventions and strategies to alleviate burnout are needed to reduce its impact on quality of patient care and overall functioning of healthcare organizations. AIM: To examine the feasibility of implementing online modules with the primary goal of determining recruitment and retention rates, completion time and satisfaction with the modules. A secondary goal was to describe changes in burnout and related symptoms associated with completing the modules. SETTING: This single-arm, nonrandomized feasibility study was conducted in five long-term care sites of a publicly-funded healthcare organization in Vancouver, British Columbia, Canada. Eligible participants were clinical staff who worked at least 1 day per month. RESULTS: A total of 103 study participants consented to participate, 31 (30.1%) of whom were lost to follow-up. Of the remaining 72 participants, 64 (88.9%) completed the modules and all questionnaires. Most participants completed the modules in an hour (89%) and found them easy to understand (98%), engaging (84%), and useful (89%). Mean scores on burnout and secondary traumatic stress decreased by .9 (95% CI: .1-1.8; d = .3) and 1.4 (95% CI: .4-2.4; d = .4), respectively; mean scores on compassion satisfaction were virtually unchanged. CONCLUSIONS: Modules that teach strategies to reduce burnout among staff in long-term care are feasible to deliver and have the potential to reduce burnout and related symptoms. Randomized controlled trials are needed to assess effectiveness and longer-term impact.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".