Non-utilization of oral health services and associated factors among children and adolescents: an integrative review
Bibliographic record
Abstract
To review publications exploring non-utilization of oral health services and to identify factors associated with non-utilization of oral health services among children and adolescents. An integrative review design was adopted. A search was conducted for research articles published during the period from 2000 to April 2021 in five databases, Medline via Ovid, Scopus, CINAHL, Cochrane Library and Web of Science. Inclusion criteria were original articles examining non-utilization of oral health services among 0–19 years old and studies published in peer-reviewed journals in English. Thematic analysis was undertaken to identify common themes. The Newcastle-Ottawa scale was used to evaluate the quality of the studies. Twenty-one geographically diverse articles were included. Nineteen studies were cross-sectional, one was a prospective cohort and one a case-control study. Non-utilization of dental health services tended to be higher in children than adolescents. There were predisposing (age, gender, ethnicity, parent’s level of education), enabling (family income, dental insurance) and need factors (subjective and objective oral health related parameters) that had been shown to be associated with non-utilization of dental services among children and adolescents. This integrative review found predisposing, enabling and need factors to be associated with dental health service non-utilization.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".