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Record W4390406581 · doi:10.1111/jgs.18727

Workforce resilience supporting staff in managing stress: A coherent breathing intervention for the long‐term care workforce

2023· article· en· W4390406581 on OpenAlexafffundabout
Brittany S. DeGraves, Heather K. Titley, Yinfei Duan, Trina Thorne, Sube Banerjee, Liane Ginsburg, Jordana Salma, Kathleen Hegadoren, Cybele Angel, Janice Keefe, Ruth A. Lanius, Carole A. Estabrooks

Bibliographic record

VenueJournal of the American Geriatrics Society · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsWestern UniversityMount Saint Vincent UniversityYork UniversityUniversity of Alberta
FundersHealthcare Excellence Canada
KeywordsMedicineAnxietyStressorIntervention (counseling)BurnoutPhysical therapyStaffingWorkforceEmotional exhaustionNursingClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Staff in long-term care (LTC) homes have long-standing stressors, such as short staffing and high workloads. These stressors increased during the COVID-19 pandemic; better resources are needed to help staff manage stress and well-being. The purpose of this study was to evaluate the effect of a simple stress management strategy (coherent breathing). METHODS: We conducted a pre-post intervention study to evaluate a self-managed coherent breathing intervention from February to September 2022. The intervention included basic (breathing only) and comprehensive (breathing plus a biofeedback device) groups. Six hundred eighty-six participants were initially recruited (359 and 327 in the comprehensive and basic groups respectively) from 31 LTC homes in Alberta, Canada. Two hundred fifty-four participants completed pre-and post-intervention questionnaires (142 [55.9%] in comprehensive and 112 [44.1%] in basic). Participants were asked to use coherent breathing based on a schedule increasing from 2 to 10 min daily, 5-7 times a week over 8 weeks. Participants completed self-administered online questionnaires pre- and post-intervention to assess outcomes-stress, psychological distress, anxiety, depression, resilience, insomnia, compassion satisfaction, compassion fatigue, and burnout. We used a mixed-effects regression model to test the main effect of time (pre- and post-intervention) and group while testing the interaction between time and group and controlling for covariates. RESULTS: We found statistically significant changes from pre- to post-intervention in stress (b = -2.5, p < 0.001, 95% CI = -3.1, -1.9), anxiety (b = -0.5, p < 0.001, 95% CI = -0.7, -0.3), depression (b = -0.4, p < 0.001, 95% CI = -0.6, -0.2), insomnia (b = -1.5, p < 0.001, 95% CI = -2.1, -0.9), and resilience (b = 0.2, p < 0.001, 95% CI = 0.1, 0.2). We observed no statistically significant differences between the two intervention groups on any outcome. CONCLUSIONS: Our findings suggest that coherent breathing is a promising strategy for improving stress-related outcomes and resilience. This intervention warrants further, more rigorous testing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.034
GPT teacher head0.400
Teacher spread0.366 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations7
Published2023
Admission routes3
Has abstractyes

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