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Integrating Psychological Well-Being and Self-Care Behaviors Into Undergraduate Nursing Education Curriculum

2024· article· en· W4414601650 on OpenAlex
Hua Li, Alana Glecia, Fiona Opoku-Mensah, Mary Ellen Labrecque, Pammla Petrucka

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

VenueNursing Education Perspectives · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCurriculumNurse educationCoping (psychology)MEDLINETeam nursingNursing research

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to review literature on integrating psychological well-being and self-care behaviors into the university undergraduate nursing education curriculum. BACKGROUND: Burnout has been recognized as a key contributing factor to nursing shortages. Interventions aiming to improve coping skills and reduce stress have been shown to be effective. Learning coping skills during nursing education would benefit students greatly during their study and beyond. METHOD: The literature review searched four electronic databases including CINAHL, Medline, Embase, and Web of Science to select relevant peer-reviewed articles. RESULTS: Seven studies met the inclusion criteria and were included in this review. Most showed that building resilience and improving self-care behaviors have positive effects on nursing students' psychological well-being. CONCLUSION: To enhance coping skills, reduce stress, and improve well-being in nursing students, university nursing programs should integrate psychological well-being and self-care behaviors into their nursing curricula.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.484
Teacher spread0.460 · 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