Parental Awareness and Use of Online Physician Rating Sites
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The US public is increasingly using online rating sites to make decisions about a variety of consumer goods and services, including physicians. We sought to understand, within the context of other types of rating sites, parents' awareness, perceptions, and use of physician-rating sites for choosing primary care physicians for their children. METHODS: This cross-sectional, nationally representative survey of 3563 adults was conducted in September 2012. Participants were asked about rating Web sites in the context of finding a primary care physician for their children and about their previous experiences with such sites. RESULTS: Overall, 2137 (60%) of participants completed the survey. Among these respondents, 1619 were parents who were included in the present analysis. About three-quarters (74%) of parents were aware of physician-rating sites, and about one-quarter (28%) had used them to select a primary care physician for their children. Based on 3 vignettes for which respondents were asked if they would follow a neighbor's recommendation about a primary care physician and using multivariate analyses, respondents exposed to a neighbor's recommendation and positive online physician ratings were significantly more likely to choose the recommended physician (adjusted odds ratio: 3.0 [95% confidence interval: 2.1-4.4]) than respondents exposed to the neighbor's recommendation alone. Conversely, respondents exposed to the neighbor's recommendation and negative online ratings were significantly less likely to choose the neighbor children's physician (adjusted odds ratio: 0.09 [95% confidence interval: 0.03-0.3]). CONCLUSIONS: Parents are beginning to use online physician ratings, and these ratings have the potential to influence choices of their children's primary care physician.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".