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Record W2904363180 · doi:10.1177/0844562118817079

Impact of Interplaying and Compounding Factors in the Novice Nurse Journey: A Basic Qualitative Research Study

2018· article· en· W2904363180 on OpenAlexaffvenueabout
Shannon Dames

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsStressorAttritionContext (archaeology)Qualitative researchNursing shortageNursingPsychologyPerceptionApplied psychologyMedicineNurse educationClinical psychology

Abstract

fetched live from OpenAlex

PURPOSE: This study explores the impact of interplaying and often compounding factors and the resulting ability to thrive as a novice nurse. Novice nurse attrition rates continue to be high, compounding concerns of an impending nursing shortage. There is currently a lack of research that seeks to understand how the interplay of contextual factors impacts novice nurses' ability to manage the stressors endemic in the field. DESIGN: The study was performed using a Basic Qualitative Research approach. Eight western Canadian novice nurses underwent multiple iterative interviews to explore the impact of interplaying contextual factors. Findings: While participant experiences and contexts vary, common patterns of interplay among factors were clear. The interplay between previous life experience factors and the workplace context has a significant impact on the perception and management of workplace stimuli. Those with more developmental assets, garnered through life experience, are less likely to experience workplace stimuli as stressful, reducing their risk of emotional exhaustion and improving their ability to thrive. Implications for nursing: Understanding how interplay impacts the ability to thrive versus survive informs new graduate transition support efforts and enables an ability to articulate the compounding nature of common novice nurse stressors.

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)0.017
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.407
GPT teacher head0.670
Teacher spread0.263 · 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 designQualitative
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

Citations12
Published2018
Admission routes3
Has abstractyes

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