MétaCan
Menu
Back to cohort

Resilience, Mindfulness and Self-Compassion: Tools for Nephrology Nurses

2021· article· en· W3186863631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNephrology Nursing Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMindfulnessBurnoutStressorCompassion fatigueWorkforceSelf-compassionNephrologyPsychological resilienceMedicineCompassionNursingResilience (materials science)PsychologyInternal medicineClinical psychologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Nephrology nurses are not immune to the effects of a stressful work environment. As a result, their emotional and psychological health can be at risk. In addition, there has been unprecedented stress and uncertainty working as nephrology nurses during the pandemic. These stressors can have negative effects on nurses' health, resulting in burnout and/or compassion fatigue, which can lead to nurses leaving nephrology or the nursing profession. Mindfulness has been suggested as a strategy to mitigate work-related stressors and build a more resilient workforce. Our experience suggests that combining self-compassion practices with mindfulness is also effective. Mindfulness may be beneficial for nephrology nurses, but its use does not negate the need for organizations to address the structural system issues that also contribute to burnout.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.395
Teacher spread0.352 · 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