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Record W2140465102 · doi:10.3928/00220124-20080101-06

Shine On: Achieving Career Satisfaction as a Registered Nurse

2008· article· en· W2140465102 on OpenAlex

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

VenueThe Journal of Continuing Education in Nursing · 2008
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNarrativeNurse educatorNursingJob satisfactionPsychologyMedical educationNurse educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

This phenomenological study focuses on the experience of career satisfaction among registered nurses. Potential participants were asked, "Do you love your work as a nurse?" A random sample of eight nurses who answered yes to this question was questioned further during semistructured conversations. Conversations were recorded and transcribed. Data collected were in the form of descriptions of times during the participants' careers when they felt most professionally fulfilled. Through narrative and poetic analysis, themes of "upholding the vulnerable," "going the extra mile," and "attending to the essential ordinary" were identified. Nurse educators play an important role facilitating career satisfaction for registered nurses. Practical implications for continuing education for educators and practicing nurses are addressed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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