Clustering of non-communicable disease risk factors among school teachers: A cross-sectional study in Kerala, India
Bibliographic record
Abstract
Background There is limited data on non-communicable disease (NCD) risk factors among teachers in low and middle-income settings. We assessed the prevalence and clustering of NCD risk factors among school teachers in Kerala. Methods This study analyzed data from 2216 school teachers in the Thiruvananthapuram district of Kerala. The World Health Organization (WHO) STEPs questionnaire for NCD risk factor surveillance was used. We collected socio-demographic information and behavioural risk factors using STEP-1 and clinical measurements using STEP-2. We included WHO recommended four behavioural risk factors and four metabolic risk factors for analysis. Results The main risk factors were physical inactivity (76.4 %) and overweight/obesity (68.8 %). Current tobacco use was reported by 5 % of men, while 13.4 % of men and 2.6 % of women reported alcohol use. A quarter of teachers consumed than five servings of fruits and vegetables daily. Self-reported diabetes prevalence was 10.6 % and dyslipidaemia was 22.4 %. Hypertension prevalence was 18.1 %. Only 2.8 % had no risk factors, 18.9 % had one, 37.7 % had two, and 40.6 % had three or more. Among physically inactive participants, the most common co-occurring risk factor was overweight, followed by insufficient fruit and vegetable intake. For overweight participants, physical inactivity and insufficient fruit and vegetable consumption were prevalent. In hypertensives, the most common co-morbidity was being overweight, followed by physical inactivity. Conclusion A higher proportion of teachers had three or more NCD risk factors indicating the need for targeted interventions for school teachers to mitigate these risk factors to prevent NCDs.
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 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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".