The impact of dental care programs on healthcare system and societal outcomes: a scoping review
Bibliographic record
Abstract
Abstract Background Dental diseases have detrimental effects on healthcare systems and societies at large. Providing access to dental care can arguably improve health outcomes, reduce healthcare utilization costs, and improve several societal outcomes. Objectives Our objective was to review the literature to assess the impacts of dental care programs on healthcare and societal outcomes. Specifically, to identify the nature of such programs, including the type of services delivered, who was targeted, where services were delivered, and how access to dental care was enabled. Also, what kind of societal and healthcare outcomes have been attempted to be addressed through these programs were identified. Methods We conducted a scoping review by searching four databases, MEDLINE, EMBASE, CINAHL, and Sociological Abstracts. Relevant articles published in English language from January 2000 to February 2022 were screened by four reviewers to determine eligibility for inclusion. Results The search resulted in 29,468 original articles, of which 25 were included in the data synthesis. We found minimal evidence that answers our proposed research question. The majority of identified programs have demonstrated effectiveness in reducing medical and dental healthcare utilization (especially for non-preventive services) and avert more invasive treatments, and to a lesser degree, resulting in cost-savings. Moreover, some promising but limited evidence about program impacts on societal outcomes such as reducing homelessness and improving employability was reported. Conclusion Despite the well-known societal and economic consequences of dental problem, there is a paucity of studies that address the impacts of dental care programs from the societal and healthcare system perspectives. MeSH terms Delivery of Health Care, Dental Care, Outcome assessment, Patient acceptance of Health Care.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".