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Record W4389509412 · doi:10.1080/21678421.2023.2291710

ALS Health care provider wellness

2023· article· nl· W4389509412 on OpenAlexafffundabout
Gregory Hansen, Sarah Burton-Macleod, Kerri Schellenberg

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2023
Typearticle
Languagenl
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersALS Society of Canada
KeywordsHealth careNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.048
GPT teacher head0.301
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes3
Has abstractyes

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