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Record W2939406501 · doi:10.1097/nnd.0000000000000522

Self-Care Strategies for Professional Development

2019· article· en· W2939406501 on OpenAlexaff
Jennifer Bonamer, Catherine Aquino‐Russell

Bibliographic record

VenueJournal for Nurses in Professional Development · 2019
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité de MonctonEmployment and Social Development Canada
Fundersnot available
KeywordsBurnoutTranscendental meditationCompassion fatigueResilience (materials science)PsychologyPsychological resilienceCompassionSample (material)Test (biology)Transcendental numberNursingProfessional developmentMeditationClinical psychologyApplied psychologyMedicineSocial psychologyPolitical sciencePedagogyMaterials science

Abstract

fetched live from OpenAlex

The Transcendental Meditation technique was provided to 27 nurses across levels and areas of the organization. Quantitative tools measured the effect on nurses' compassion fatigue and resilience. Paired-sample t test analyses revealed significant improvements in the phenomena studied. Further, resilience showed a large significant, inverse relationship with burnout. This study contributes new knowledge for an innovative self-care strategy to improve resilience and reduce compassion fatigue and burnout among nurses.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.028
GPT teacher head0.442
Teacher spread0.414 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations37
Published2019
Admission routes1
Has abstractyes

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