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Record W2562611989 · doi:10.1177/0969733014523167

Work engagement in nursing practice

2014· article· en· W2562611989 on OpenAlexaff
Kacey Keyko

Bibliographic record

VenueNursing Ethics · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWork engagementWork (physics)PsychologyHealth careNursingPerspective (graphical)Value (mathematics)Engineering ethicsMedicinePolitical science

Abstract

fetched live from OpenAlex

The concept of work engagement has existed in business and psychology literature for some time. There is a significant body of research that positively correlates work engagement with organizational outcomes. To date, the interest in the work engagement of nurses has primarily been related to these organizational outcomes. However, the value of work engagement in nursing practice is not only an issue of organizational interest, but of ethical interest. The dialogue on work engagement in nursing must expand to include the ethical importance of engagement. The relational nature of work engagement and the multiple levels of influence on nurses' work engagement make a relational ethics approach to work engagement in nursing appropriate and necessary. Within a relational ethics perspective, it is evident that work engagement enables nurses to have meaningful relationships in their work and subsequently deliver ethical care. In this article, I argue that work engagement is essential for ethical nursing practice. If engagement is essential for ethical nursing practice, the environmental and organizational factors that influence work engagement must be closely examined to pursue the creation of moral communities within healthcare 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 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)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.027
Scholarly communication0.0120.007
Open science0.0010.016
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.337
GPT teacher head0.619
Teacher spread0.282 · 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 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

Citations39
Published2014
Admission routes1
Has abstractyes

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