Sociodemographics and School Environment Correlates of Clustered Oral and General Health Related Behaviours in Tanzanian Adolescents
Bibliographic record
Abstract
Objectives: To identify underlying clusters of general and oral health behaviours and acertain possible factors influencing the existence of the behaviours. Materials and Methods: A cross sectional study was conducted among 4,847 school adolescents aged 11 to 17 years. Data were collected using a structured questionnaire in Kiswahili inquiring about general and oral health related behaviours, socio-demographics and adolescents’ school relationship. Principal component analysis was employed to identify clusters of health behaviour. Frequency distribution for proportions, cross tabulations with chi-square and a two stage binary logistic regression were done. Results: Principal component analysis identified four clusters from twelve health behaviours; hygiene practices, dietary behaviours, cigarette smoking & alcohol consumption and sedentary related behaviours. Girls, OR 0.8 (95% CI 0.7, 0.9); secondary school attendees, OR 0.5 (95% CI 0.4, 0.7) and adolescents with good school relationship OR 0.7 (95% CI 0.6, 0.8) were less likely to smoke or use alcohol. Urban residents were less likely OR 0.8, (95% CI 0.7, 0.9) to report acceptable dietary behaviours. Adolescents whose fathers had secondary education or higher, were in secondary schools and had good school relationship were most likely to have acceptable hygiene behaviours, OR 1.4 (95% CI 1.2, 1.6), 1.6 (95% CI 1.1, 2.2) and 1.4 (95% CI 1.3, 1.7), respectively. Conclusion: Oral and general health behaviours of Tanzanian adolescents factored into four clusters with hygiene behaviours being most practiced and physical exercise the least. The clustered behaviours were influenced by socio-demographics and school environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".