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Record W190033793

Work Life Balance Among Nurse Educators Towards Quality Life: A Mixed Method Study

2014· article· en· W190033793 on OpenAlexaff
Eddieson Pasay‐an, Petelyne Pangket, Juanita Yudong Nialla, Lynn B Laban

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsWork–life balanceWork (physics)NursingHuman multitaskingPersonal lifeBalance (ability)PsychologyQuality of life (healthcare)Medical educationMedicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Work-Life balance is completely an imminent problem that needs to be addressed across all organizations. The nursing field, especially nurses in the Academe is not excused due to multiple roles they are facing. This study was intended to determine and explore the work life balance among nurse educators towards quality life. The respondents of the study were the nurse educators of the Schools of Nursing in the city Baguio and the province of Benguet, Philippines. The research utilized Mixed Method design specifically, sequential explanatory strategy. It was found out that work-life balance of nurse educators vary and that nurse educators can maintain their composure in their work with or without interference with personal life or vice versa despite their very complex roles. Further exploring the verbatim accounts of the participants, the researchers extracted participants’ significant statements and organized into themes. Three main themes surfaced as similar among the participants: Time scheduling, demarcation of work and life and multitasking. It is recommended therefore that nurse educators should maintain their composure towards quality work and life despite their complex roles. To do this, they should put demarcation or boundary in their work and personal life.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.390
Teacher spread0.343 · 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 designObservational
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

Citations7
Published2014
Admission routes1
Has abstractyes

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