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Record W2806529637 · doi:10.1111/jan.13729

Mindfulness‐based stress reduction training yields improvements in well‐being and rates of perceived nursing errors among hospital nurses

2018· article· en· W2806529637 on OpenAlexaff
Stéphanie Daigle, France Talbot, Douglas J. French

Bibliographic record

VenueJournal of Advanced Nursing · 2018
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsAtlantic Cancer Research InstituteContinental (Canada)Université de Moncton
Fundersnot available
KeywordsMindfulness-based stress reductionMindfulnessRandomized controlled trialMedicineDistressGuided imageryNursingStress reductionClinical psychologyAnxietyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: This pilot study aims to further document mindfulness-based stress reduction (MBSR)'s effect on well-being while exploring its impact on errors among hospital nurses. BACKGROUND: The concept of mindfulness has been found to be highly relevant to holistic nursing practices but remains understudied and underused. Preliminary evidence suggests that MBSR can reduce stress among nurses. As stress and mental processes such as inattention are potential sources of error, MBSR may also help to improve patient safety. Reducing errors is of significant relevance in healthcare settings. DESIGN: A randomized controlled trial with a matched pair design was conducted. METHODS: Seventy Registered Nurses and licensed practical nurses were randomized to MBSR (N = 37) or a waitlist control condition (N = 33). RESULTS: Intention-to-treat ANCOVAs revealed that MBSR produced significant improvements in distress. High levels of treatment satisfaction were reported by a majority of participants. Of the nurses who reported that errors had been a problem for them (28.6%), a perceived improvement was noticed by over a third (37.5%) at 3 months post-treatment. CONCLUSION: These initial findings suggest that the benefits of MBSR may extend to nursing errors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.352
Teacher spread0.331 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations55
Published2018
Admission routes1
Has abstractyes

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