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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".