MétaCan
Menu
Back to cohort
Record W2735876901 · doi:10.1111/jnu.12323

New Graduate Nurses’ Professional Commitment: Antecedents and Outcomes

2017· article· en· W2735876901 on OpenAlexaffabout
Sylvie Guerrero, Denis Chênevert, Steven Kilroy

Bibliographic record

VenueJournal of Nursing Scholarship · 2017
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsHEC MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychologyOrganizational commitmentGraduate studentsNursingMedical educationSocial psychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

PURPOSE: This study examines the factors that increase new graduate nurses' professional commitment and how this professional commitment in turn affects professional turnover intentions, anxiety, and physical health symptoms. DESIGN: The study was carried out in association with the nursing undergraduate's affiliation of Quebec, Canada. A three-wave longitudinal design was employed among nursing students. Nurses were surveyed before they entered the labor market, and then twice after they started working. METHODS: Participants were contacted by post at their home address. The hypotheses were tested using structural equation modeling. FINDINGS AND CONCLUSION: Professional commitment explains why good work characteristics and the provision of organizational resources related to patient care reduce nurses' anxiety and physical symptoms, and increase their professional turnover intentions. Pre-entry professional perceptions moderate the effects of work characteristics on professional commitment such that when participants hold positive pre-entry perceptions about the profession, the propensity to develop professional commitment is higher. CLINICAL RELEVANCE: There is a worldwide shortage of nurses. From a nurse training perspective, it is important to create realistic perceptions of the nursing role. In hospitals, providing a good work environment and resources conducive to their professional ethos is critical for ensuring nurses do not leave the profession early on in their careers.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.803
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.109
GPT teacher head0.434
Teacher spread0.324 · 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.

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

Citations61
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nursing ScholarshipSame topicNursing education and managementFrench-language works237,207