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Record W3094759245 · doi:10.1515/ijnes-2020-0021

Self-compassion in undergraduate nursing: an integrative review

2020· review· en· W3094759245 on OpenAlexaff
Lisa-Anne Hagerman, Louela Manankil‐Rankin, Jasna Schwind

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2020
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsToronto Metropolitan UniversityNipissing UniversityConestoga College
Fundersnot available
KeywordsMindfulnessCompassionSelf-compassionCurriculumExperiential learningPsychologyNursingNurse educationMedical educationPsychotherapistMedicinePedagogy

Abstract

fetched live from OpenAlex

Objective To explore self-compassion and its role in supporting well-being, compassionate care, and the academic experience in undergraduate nursing students. Method Whittemore and Knafl's (2005) integrative review methodology was used to search articles published between 2007 and 2020, which resulted in 36 articles meeting the inclusion criteria: compassion for self and others, strategies to support self-compassion; and self-compassion and student learning. Result Findings indicate that self-compassion may promote compassionate care, personal well-being, resilience, and emotional intelligence while supporting indicators of academic success. Compassion literacy, mindfulness training, and experiential exercises are some of the strategies that could be integrated into nursing curricula to enhance compassion in nursing students for self and others. Conclusion Integrating mindfulness and self-compassion in undergraduate curricula requires innovative teaching and learning approaches within a supportive organizational environment. To this end, a Self-Compassion Curricular Model to guide nursing programs is proposed.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.144
GPT teacher head0.522
Teacher spread0.379 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations35
Published2020
Admission routes1
Has abstractyes

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