ALS Health care provider wellness
Bibliographic record
Abstract
BACKGROUND: Interest in health care provider (HCP) wellness and burnout is increasing; however, minimal literature explores HCP wellness in the context of Amyotrophic Lateral Sclerosis (ALS) care. OBJECTIVES: We sought to determine rates of burnout and resiliency, as well as challenges and rewards in the provision of ALS care. METHODS: A survey link was sent to physicians at all Canadian ALS centers for distribution to ALS HCPs in their network. The survey included demographics questions, and validated measures for resiliency and burnout; the Brief Resilient Coping Scale (BRCS) and the Single Item Burnout Score (SIBS). Participants were asked to describe challenges and rewards of ALS care, impact of COVID-19 pandemic, and how their workplace could better support them. RESULTS: = 48 (56.5%), but resiliency was not related to burnout. Challenges included feeling helpless while patients relentlessly progressed to death, and emotionally charged interactions. Participants found fulfillment in providing care, and through relationships with patients and colleagues. There was a strongly expressed desire for increased resources, team building/debriefing, and formal training in emotional exhaustion and burnout. CONCLUSIONS: The high rate of burnout and challenges of ALS care highlight the need for additional resources, team-building, and formal education around wellness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".