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Record W2315771427 · doi:10.1177/1744987115624135

The influence of authentic leadership and supportive professional practice environments on new graduate nurses’ job satisfaction

2016· article· en· W2315771427 on OpenAlexaffabout
Fatmah Fallatah, Heather K. Spence Laschinger

Bibliographic record

VenueJournal of research in nursing · 2016
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsWestern University
Fundersnot available
KeywordsJob satisfactionWorkforceNursingAuthentic leadershipPsychologyNursing shortageMedical educationMedicineNurse educationPolitical science

Abstract

fetched live from OpenAlex

It is important to uncover new approaches to attracting and retaining newly qualified nurses in Canada to address the growing nursing workforce shortage. Authentic leadership theory proposes mechanisms that allow managers to create positive and supportive environments that facilitate new graduate nurses’ transition into practice and subsequently improve nurses’ and organizational outcomes. The purpose of this study was to test a theoretical model linking authentic leadership to new graduate nurses’ job satisfaction through its effect on supportive professional practice environments. A secondary analysis of data ( n = 93) from a larger study of new graduate nurses in their first two years of practice was conducted. Mediation multiple regression analysis was performed to determine the influence of authentic leadership and supportive professional practice environments on new graduate nurses’ job satisfaction. Measures of the Authentic Leadership Questionnaire (ALQ), the Revised Nursing Worklife Index (NWI-R) and the North Carolina Center for Nursing – Survey of Newly Licensed Nurses (NCCN-SNLN) were used. Supportive professional practice environment partially mediated the relationship between authentic leadership and new graduate nurses’ job satisfaction. The findings suggest that managers who demonstrate authentic leadership create supportive professional practice environments and are more likely to enhance new graduate nurses’ job satisfaction.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.933
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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)0.0000.000

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 head0.117
GPT teacher head0.461
Teacher spread0.344 · 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

Citations67
Published2016
Admission routes2
Has abstractyes

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