Dimensional Analysis of Psychosocial Barriers to Prevention of Early Childhood Caries Among Recent Immigrants
Bibliographic record
Abstract
The objective of this article is to define the underlying dimensions of psychosocial barriers to obtaining and providing dental care for young children among recent immigrants. Fifteen focus groups were conducted with 99 primary caregivers from African, South Asian, and Chinese recent immigrants. A secondary analysis of identified barriers using dimensional analysis methodology was performed to determine dimensions and properties of barriers. The analysis continued until irreducible properties were found or emerging dimensions were not relevant to the study. Identified dimensions were associated with barriers and individuals. Type, number, level, objectiveness, nature, and impact were barrier-related; awareness and controllability were individual-related dimensions. Type refers to barriers themselves. Number and level indicate the amount and location of barriers, respectively. Objectiveness refers to the extent that perceived barrier reflects reality and nature indicates its intrinsic characteristic. Impact concerns behaviors, goals, and outcomes compromised by barriers. Awareness alludes to the extent that individuals are aware of the barriers and controllability explains how much control people perceive to have over barriers. Identified dimensions are useful for better understanding and addressing existing barriers to children’s optimal oral health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".