Oral Health Care Out-of-Pocket Costs and Financial Hardship: A Scoping Review
Bibliographic record
Abstract
The objective of this study is to characterize how financial hardship related to oral health care (OHC) out-of-pocket (OOP) spending has been conceptualized, defined, and measured in the literature and to identify evidence gaps in this area. This scoping review follows Arksey and O'Malley's framework and synthesizes financial hardship from OHC concepts, methodologies, and evidence gaps. We searched Ovid-Medline, Ovid-Embase, PubMed, Web of Science, Scopus, EconLit, Business Source Premier, and the Cochrane Library. Gray literature was sourced from institutional websites (World Health Organization, United Nations, World Bank Group, Organisation for Economic Co-operation and Development, and governmental health agencies) as well as ProQuest Dissertations and Thesis Global. We used defined inclusion and exclusion criteria to select studies published between 2000 and 2023. Of the 1,876 records, 65 met our criteria. The studies conceptualized financial hardship as catastrophic spending, impoverishment, negative coping strategies, bankruptcy, financial burden, food insecurity, and personal financial hardship experience. We found heterogeneity in defining OHC OOP payments and services. Also, financial hardship was frequently measured as catastrophic health expenditure using cross-sectional designs and national household spending surveys from high-income and to a lesser extent lower-middle-income countries. We identify and discuss challenges in terms of conceptualizing financial hardship, study designs, and measurement instruments in the OHC context. Some of the common evidence gaps identified include studying the causal relationship in financial hardship from OHC, assessing the financial hardship and unmet dental needs due to cost relationship, and distinguishing the effect between pain/discomfort and esthetic/cosmetic dental treatments on financial hardship. Financial hardship in OHC needs further exploration and the use of consistent definitions as well must distinguish between treatments alleviating pain/discomfort from esthetic/cosmetic treatments. Our study is relevant for policy makers and researchers aiming to monitor financial protection of OOP payments on OHC in the wake of universal health coverage for 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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".