US direct-to-consumer medical service advertisements fail to provide adequate information on quality and cost of care
Bibliographic record
Abstract
BACKGROUND: In the 1970s, the Federal Trade Commission declared that allowing medical providers to advertise directly to consumers would be "providing the public with truthful information about the price, quality or other aspects of their service." However, our understanding of the advertising content is highly limited. OBJECTIVE: To assess whether direct-to-consumer medical service advertisements provide relevant information on access, quality and cost of care, a content analysis was conducted. METHOD: Television and online advertisements for medical services directly targeting consumers were collected in two major urban centres in Nevada, USA, identifying 313 television advertisements and 200 non-duplicate online advertisements. RESULTS: Both television and online advertisements reliably conveyed information about the services provided and how to make an appointment. At the same time, less than half of the advertisements featured insurance information and hours of operation and less than a quarter of them contained information regarding the quality and price of care. The claims of quality were substantiated in even fewer advertisements. The scarcity of quality and cost information was more severe in television advertisements. CONCLUSION: There is little evidence that medical service advertising, in its current form, would contribute to lower prices or improved quality of care by providing valuable information to consumers.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".