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Record W4366364969 · doi:10.1111/nhs.13021

Sociodemographic and academic factors associated with unhealthy lifestyle among Brazilian nursing students

2023· article· en· W4366364969 on OpenAlexaff
Tássia Teles Santana de Macêdo, Debra Sheets, Fernanda Michelle Santos e Silva Ribeiro, Carlos Antônio de Souza Teles Santos, Ana Luísa Patrão, Fernanda Carneiro Mussi

Bibliographic record

VenueNursing and Health Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of Victoria
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMultinomial logistic regressionMedicineLogistic regressionCross-sectional studyHealth promotionDemographyGerontologyNursingPublic health

Abstract

fetched live from OpenAlex

This study aimed to identify sociodemographic and academic factors associated with unhealthy lifestyles among Brazilian undergraduate nursing students. A cross-sectional study was completed by 286 nursing students in Brazil. Multinomial logistic regression was conducted to examine the association between sociodemographic and academic variables with the latent lifestyle indicator. The model fit's validity was assessed using Akaike information coefficient estimation, Hosmer-Lemeshow test, and the ROC curve. A high health risk lifestyle was 2.7 times more likely among students aged 18-24 years than students aged 25 years or older (OR = 2.7, 95% CI = [1.18, 6.54] p = 0.02); 2.3 times more likely among students with ≥400 h of semester time (OR = 2.3, 95% CI = [0.93, 5.90], p = 0.07); and 3.8 times more likely among female students (OR = 3.8, 95% CI = [0.82, 8.12], p = 0.09). A moderate health risk lifestyle was 1.8 times more likely among students from the 6th to 10th semesters (OR = 1.8, 95% CI = [-0.95, 3.75], p = 0.07). Sociodemographic and academic factors were associated with unhealthy lifestyles. Health promotion efforts are necessary to improve nursing students' health behaviors.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
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 head0.173
GPT teacher head0.539
Teacher spread0.366 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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