Children’s access to dental care during the COVID-19 pandemic: a multi-country survey
Bibliographic record
Abstract
We assessed the impact of COVID-19 on children’s access to dental care and determine factors associated with problems in accessing dental care. A multi-country cross-sectional survey collected data from caregivers of children from August 2020 to February 2021. The questionnaire was developed guided by the framework of the Andersen’s model of factors (predisposing, enabling and need). Multilevel logistic regression was used to assess the association between access-to-dental care problem and predisposing, enabling and need factors. A total of 4,843 caregivers from 20-countries reported on their children (52.3% males, mean age = 8.4 years) with 29.2% having access to care problem. A significantly greater percentage of caregivers from lower-middle-income countries (LMICs) than low-income countries (LICs), upper-middle-income countries (UMICs) and high-income countries (HICs) reported an access-to-dental care problem (P < .001). Caregivers living in LICS, university-educated caregivers, caregivers with older children and caregivers whose children had more frequent pain during the pandemic had higher odds of reporting an access to dental care problem. The association of the access problem with dental pain and dental insurance was modified by country income, showing a link between macrolevel contextual factors and the utilization of dental care in children that needs to be addressed in future studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".