A life‐course approach to assess psychosocial factors and periodontal disease
Bibliographic record
Abstract
BACKGROUND: Several models have been used to suggest the role of psychosocial factors in periodontal disease. None have adopted the life-course approach, which emphasizes the importance of exposures over time and at critical points of a person's life. OBJECTIVE: To investigate the relationship between psychosocial factors at two periods of life and periodontal diseases in Brazilian adult females. MATERIAL AND METHODS: The study design was a cross-sectional survey of 330 women randomly selected from a larger sample of mothers whose children participated in a study on chronic oral disease using a life-course framework. Each woman was clinically assessed for the presence of periodontal disease. An interview collected information on socioeconomic, behavioural and family-related factors at two periods of the participant's life (childhood and adulthood). The main outcome variable was loss of periodontal attachment. Data analysis used logistic regression. RESULTS: High levels of periodontal disease were predicted by <4 years of education, past and present smoking, high levels of paternal discipline in childhood and low levels of emotional support in adulthood. The influence of childhood factors was not attenuated by adulthood circumstances. CONCLUSION: Psychosocial factors in childhood and adulthood were associated with high levels of periodontal disease in adulthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".