Lifestyle behaviors among undergraduate nursing students: A latent class analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it