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Record W2899682112 · doi:10.29173/jpnep20

Emotional Self-Management Experiences of Practical Nursing Students

2022· article· en· W2899682112 on OpenAlexaffabout
Cindy Ko

Bibliographic record

VenueJournal of Practical Nurse Education and Practice · 2022
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsNiagara College
Fundersnot available
KeywordsEmotional intelligencePhenomenology (philosophy)PsychologyCurriculumFace (sociological concept)Hermeneutic phenomenologyNursingMedical educationPedagogySocial psychologyLived experiencePsychotherapistMedicineSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

This paper is part of a doctoral dissertation based on a 2017 phenomenological study that explored practical nursing (PN) students’ lived experiences with emotional self-management in clinical settings using van Manen’s orientation to hermeneutic phenomenology. A review of PN program curricula in Ontario, Canada, suggested that they do not specifically include emotional intelligence (EI) and the core concept of emotional self-management. Mayer’s and Salovey’s original four-branch ability model of EI was used as the theoretical framework. Face-to-face interviews were conducted with a purposive convenience sample of 10 PN students at a southern Ontario community college. Findings suggested that the participants perceived themselves to have basic EI knowledge. Participants expressed that their first knowing, in the phenomenological sense, of EI provided them with more confidence and awareness. An increased understanding of emotional self-management could enhance teaching and learning approaches, particularly with PN students who are exposed to high-stress clinical environments.

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)0.002
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.504
Teacher spread0.446 · 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.

Study designQualitative
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

Citations5
Published2022
Admission routes2
Has abstractyes

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