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Record W3175256900 · doi:10.1177/08445621211028076

Sources of Stress and Coping Strategies Among Undergraduate Nursing Students Across All Years

2021· article· en· W3175256900 on OpenAlexaffvenueabout
Mélanie Lavoie‐Tremblay, Lia Sanzone, Thalia Aubé, Maxime Paquet

Bibliographic record

VenueCanadian Journal of Nursing Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsCoping (psychology)PsychologyNursingStress (linguistics)Medical educationMedicineClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Undergraduate nursing students experience high levels of stress during their programs. The literature on their stress is extensive, however, what is less well-known are the specific sources of stresses for students in different years of study. PURPOSE: The aim of this study is to understand nursing students' sources of stress and coping strategies in each year of study. METHOD: A transversal descriptive qualitative study was used. A sample of 26 undergraduate students attending a university in Montreal, Canada were recruited and participated in a semi-structured interview. Data were analysed using inductive thematic analysis. RESULTS: The sources of stress differed according to year of study and related significantly to the specific novelty of that year. For first-year students, their stress was related to their academic courses. High clinical performance expectations and a lack of time for their personal lives was a main source of stress for second-year students. The prospect of graduating and transitioning into the work environment caused stress for students in their final year. Students across all years of study utilized similar coping strategies. CONCLUSION: The findings suggest that interventions can be developed to address the sources of stress experienced by nursing students in each year of study.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.191
GPT teacher head0.560
Teacher spread0.370 · 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

Citations116
Published2021
Admission routes3
Has abstractyes

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