Risks for non-communicable chronic diseases: A cross-sectional study with undergraduate nursing students
Bibliographic record
Abstract
Background: The situational analysis is one of the essential competencies of the nurse as a health promoter to ensure planning by the use of adequate strategies, coherent approaches and attainable goals in a community. The present study focused on performing a situational analysis of lifestyles related to the risk factors for non-communicable chronic diseases (NCCD) with undergraduate nursing students. Methods: A cross-sectional study developed with 77 female nursing undergraduates where the sociodemographic variables, physical conditions and lifestyles inherent to the risk for non-communicable chronic diseases were investigated. Results: Female undergraduates between the ages of 20 and 24 years prevailed in the study. They were white, single, belonged to social class B and were in the 3rd and 4th year of the course. Though sedentary, the students had their weight fitting their height and their waist circumference values within normal standards. In addition to this, they stated being non-smokers, besides presenting low risk regarding alcohol consumption. Conclusions: It is worth noting the nurse’s role as a health promoter at the development of encouragement strategies for healthy life practices and the planning of interventions against sedentarism.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".