Influence of the Socio-Economic Context on Self-Reported Gingival Bleeding in Individuals of Ethnic Minority Groups: A Multilevel Analysis
Bibliographic record
Abstract
INTRODUCTION: To evaluate the influence of the socio-economic context on self-reported gingival bleeding (SRGB) in individuals of ethnic minority groups (IEG). METHODS: Data from the 2007 National Public Health Survey in Colombia were collected. A multiple-stage stratified sampling was used. Data from 34.843 subjects were collected through interviews. The influence of socio-economic factors on SRGB in IEG was investigated with logistic and multilevel regression analyses. RESULTS: Out of 34.843 subjects studied, a total of 6.440 individuals were members of ethnic minority groups. SRGB was observed in approximately 5% of IEG. There was a significant difference between IEG and subjects of the rest of the sample (28.403 subjects) regarding SRGB, elementary and high school education, Gross Domestic Product (GDP), Human Development Index (HDI) and Unmet Basic Needs Index (UBNI) disfavouring IEG (P<0.05). The logistic model showed that SRGB was associated with IEG (P<0.001). This association persisted after controlling for confounders. A total of 33 Colombian states (level 2) and 6.440 members (level 1) of ethnic minority groups were included in the multilevel analisys; this model showed that the variance on SRGB was statistically significant at level 1 and 2. However, the variation at IEG level (35%) was smaller than the variation between states (65%) in the multilevel multivariate model. CONCLUSIONS: SRGB was higher in IEG. Also, GDP, HDI and UBNI were unfavourable factors in the members of ethnic minority groups. Considering these detriment factors and the higher variation between states, this study suggests that socio-economic context affects significantly SRGB in IEG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".