Willingness to Pay for Mandibular Overdentures: A Societal Perspective
Bibliographic record
Abstract
OBJECTIVES: Dental services in many countries are funded out-of-pocket by patients whose acceptance of a dental treatment depends on their valuation of it. Using a willingness-to-pay (WTP) strategy, this study aimed to determine how people who do not wear dentures value the benefits of dentures retained by implants and what factors explain variations in WTP among subjects. METHODS: Telephone numbers of a representative Canadian sample were obtained from a consumer database provider. Respondents completed either an internet-based or telephone survey with 3 payment scenarios: paying oneself (out-of-pocket), coverage with private health insurance, and publicly financed through additional taxes. Personal information data (e.g., age, income) were used as independent variables in regression models to assess the determinants of WTP amounts. RESULTS: Among 1,096 respondents, 317 participated in the survey (response rate, 28.9%). The mean WTP of participants (mean ± SD age: 41.2 ± 0.6 y; 54.3% male) who were dentate/partially edentate was $5,347 for implant overdentures. Considering a 1 in 5 chance of becoming edentate, they were willing to pay $26.93 as monthly payments for private insurance. They were also willing to pay an additional yearly tax of $103.63 to support a public program. WTP private payments increased substantially with increase in household income and dental needs. CONCLUSION: This preference study provides information to dentists, insurance companies, and policy makers on what dentate people are willing to pay for implant overdentures, whether directly or with insurance/government coverage. KNOWLEDGE TRANSFER STATEMENT: This study provides results of interest to many stakeholders. For clinicians, the results reveal what people are willing to pay for implant overdentures for themselves. It also provides information to employers and insurance companies on how people value having coverage for this kind of service. Furthermore, it provides public policy makers the value that people place on public funding of such treatments and how they would support a decision to publicly fund such a treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".