Weigh More, Pay More? Public Opinion on Varying Health Insurance Contributions among Divergent Weight Groups
Bibliographic record
Abstract
BACKGROUND: The prevalence of obesity and its related costs has increased over the past decades. In Germany, obesity-related costs are merely covered by statuary health insurance. Within the statutory health care system, the health insurance contributions do not differ between people with and without health issues, such as being obese. This study aims to investigate the public's opinion about whether people with obesity should pay a higher proportional health care contribution than people of normal weight. METHODS: We conducted a pilot study and collected thereof data of a convenience sample. In total, 179 participants who perceived themselves to be of normal weight (51.40% female; mean age = 32.46, SD = 5.74) were surveyed using a questionnaire. Within this questionnaire, the participants had to rate how high the proportional health care contribution for people with and without obesity should be. Moreover, we assessed participants' antifat attitudes by applying the Fat Phobia Scale and the Implicit Association Test. RESULTS: A paired t test revealed that participants suggest a significantly higher proportional contribution for health insurance for people with obesity compared to people with normal weight (t(178) = 4.51, p < 0.001). Logistic regression analysis indicates that people with stronger explicit (OR = 8.77, p < 0.001) and implicit stigma (OR = 1.06, p = 0.018), and higher BMI (OR = 1.27, p = 0.04) are more likely to suggest an increased contribution rate for people with obesity. CONCLUSION: Although we found that participants suggested higher contribution rates for people with obesity, overall only one-quarter of the participants suggested higher contribution rates for people with obesity, whereas almost three-quarters of the participants did not distinguish the contribution rate for people with and without obesity. Moreover, we found that the participants called for higher insurance premiums for people with and without obesity. Therefore, future studies should consider giving more information about the statutory health care system or the health care contribution rate before asking participants about their opinion.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".