Lifestyle behaviors among undergraduate nursing students: A latent class analysis
Bibliographic record
Abstract
This is a cross-sectional study whose objective was to identify clustering of lifestyle behaviors among undergraduate nursing students to inform health promotion efforts and improve health outcomes later in life. All 353 undergraduate nursing students from the School of Nursing in a public university, Bahia, Brazil were invited to participate. The inclusion and exclusion criteria were according to the major project. Participants must be enrolled and attending the 1st to 10th semester, with a minimum age of 18 years. Participants were excluded if they had any physical disabilities that limited the collection of anthropometric measures or were completing an internship off-campus. A total of 286 undergraduate nursing students met the criteria and completed the survey. The questionnaires included standardized measures for demographic, academic, and lifestyle behaviors (e.g., tobacco use, alcohol use, physical activity level, sedentary behavior, and fruits and vegetables consumed). Latent class analysis was performed to identify any clustering of lifestyle behaviors. Descriptive analyses indicated that 3.1% of the students were smokers, 23.1% consumed alcohol, 34.3% were inactive, 85.0% were sedentary, and 80.8% did not consume recommended amounts of fruits and vegetables. Latent class analysis produced four distinct subtypes of health risk: (a) low-health risk (33.57%); (b) moderate-health risk (27.97%); (c) high-health risk (19.58%); and (d) very high-health risk (18.88%). Approximately 38.5% of students were in the very high or high-risk classes. The proportion of students with very high and high-health risks emphasizes the importance of health promotion programs for university nursing students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".