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Record W2974107960 · doi:10.5430/jnep.v10n1p11

Sleep quality of Brazilian nursing students: A cross-sectional study

2019· article· en· W2974107960 on OpenAlexvenueno aff
Andréia Ferreira dos Santos, Fernanda Carneiro Mussi, Cláudia Geovana da Silva Pires, Melissa Almeida Santos Paim, Fernanda Michelle Santos e Silva, Rodrigo Marques da Silva

Bibliographic record

VenueJournal of Nursing Education and Practice · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Burnout
Canadian institutionsnot available
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexSleep (system call)Cross-sectional studySleep qualityPsychological interventionMedicineQuality (philosophy)PsychologyNursing Interventions ClassificationNursingGerontologyDemographyPsychiatryInsomniaSociology

Abstract

fetched live from OpenAlex

Objective: To describe the sleep quality of nursing students according to the Pittsburgh Sleep Quality Index.Methods: This is a cross-sectional study conducted with 286 nursing students from a public institution in Salvador, Bahia. Data gathering instruments were applied in classrooms and data were assessed in absolute and relative frequencies, means and standard deviation.Results: The mean age of participants was 23.48 years (SD = 4.421). Most of students were female (90.2%), single with partner (90.9%), afro descendent (87.8%), unemployed (81.5%), total Family income below than four minimum wages (47.2%), enrolled between 6th and 10th semester (54.5%), attending academic activities in two or three shifts (80.8%). Sample showed poor sleep quality (86.4%), especially due to the sleep duration, sleep disturbances and daytime disfunction.Conclusions: Poor sleep quality was prevalent in the sample, what rises the need of further analysis of the associated factors and interventions to change this reality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.210
GPT teacher head0.646
Teacher spread0.436 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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