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Record W4281685097 · doi:10.5430/jnep.v12n10p16

The impact of nursing students’ sleep hygiene practices on patient safety

2022· article· en· W4281685097 on OpenAlexvenueno aff
Cynthia M. Thomas, Constance E. McIntosh, Ruthie LaMar

Bibliographic record

VenueJournal of Nursing Education and Practice · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsSleep hygieneLikert scaleNursingMedicineSleep (system call)CognitionHygienePatient safetyDescriptive statisticsNurse educatorPsychologyHealth careNurse educationPsychiatrySleep qualityDevelopmental psychology

Abstract

fetched live from OpenAlex

Background: Nursing students are prone to inadequate sleep but not fully aware of personal health risks, potential safety and quailty of care issues. Poor sleep hygiene can impact cognition, aleartness, cognitive speed, and accuracy of tasks completion, lower grades, fatigue and depression.Methods: This descriptive study addressed quantitative data from a 4-point Likert scale and open-ended questions. Nursing students from the National Student Nurse Association enrolled in an associate or baccalaureate program and having had at least one clinical experience were invited to particiate in the study.Results: Results indicate the amount of sleep needed is not being achieved. Participants reported ingesting substances to stay awake and to induce sleep. Nineteen percent of students reported making an error during a clinical experience.Conclusions: Students may be naive in thinking short- and long-term use of sleep-inducing aides and stimulants for wakefulness pose no risks to personal safety and safety of patients. By identifying and addressing systemic causes of nursing students lack of sleep using a comprehensive approach to educate, impose consequences, and promote sleep hygiene at the local and national levels, students will have fewer reasons and justifications for not achieving adequate sleep.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.078
GPT teacher head0.484
Teacher spread0.406 · 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 designOther design
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

Citations4
Published2022
Admission routes1
Has abstractyes

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