A cross sectional study of Mexican caregiver social status, dental knowledge, self‐efficacy, and caregiver/child oral health. A structural equation model
Bibliographic record
Abstract
OBJECTIVES: To test hypothetical models relating caregivers' social status, knowledge, and self-efficacy to caregiver/child (C/C) oral health-related outcomes. METHODS: One hundred fifty C/C pairs participated (recruitment = 87.7 percent). Three C/C outcomes were clinically assessed: "Oral self-care"; "Functional dentitions"; and "Dental treatment needs." Information about caregiver (CG) social status, knowledge, and self-efficacy was also gathered. Structural equation modeling tested measurement models (MMs) for CG social status, CG knowledge, and CG self-efficacy. The structural models (SMs) hypothesized causal paths among CG social status, CG knowledge, CG self-efficacy, and C/C oral health outcomes. RESULTS: estimates, Goodness of Fit Index >0.95, Normed Fit Index ~ >0.95, Confirmatory Fit Index >95, Root Mean Square Error Approximation <0.05). For the SMs, the best overall fit was for "Functional dentitions," while SMs for "Oral self-care," and "Dental treatment needs" required revisions. In all the SMs, the path between "Caregiver social status" and "Caregiver knowledge" was significant. In the "C/C Functional dentitions" SM, the significant path linked "CG self-efficacy" and "Child functional dentition." In the "C/C Dental treatment needs" SM, the significant path linked "CG self-efficacy" and "CG functional dentition." CONCLUSIONS: Hypothetical models for three oral health-related outcomes were partly or fully validated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".