Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter
Bibliographic record
Abstract
BACKGROUND: Dental care expenses are reported to present higher financial barriers than any other type of health care service in the United States. Social media platforms such as Twitter have become a source of public health communication and surveillance. Previous studies have demonstrated the usefulness of Twitter in exploring public opinion on aspects of dental care. To date, no studies have leveraged Twitter to examine public sentiments regarding dental care affordability in the United States. OBJECTIVE: The aim of this study is to understand public perceptions of dental care affordability in the United States on the social media site, Twitter. METHODS: Tweets posted between September 1, 2017, and September 30, 2021, were collected using the Snscrape application. Query terms were selected a priori to represent dentistry and financial aspects associated with dental treatment. Data were analyzed qualitatively using both deductive and inductive approaches. In total, 8% (440/5500) of all included tweets were coded to identify prominent themes and subthemes. The entire sample of included tweets were then independently coded into thematic categories. Quantitative data analyses included geographic distribution of tweets by state, volume analysis of tweets over time, and distribution of tweets by content theme. RESULTS: A final sample of 5314 tweets were included in the study. Thematic analysis identified the following prominent themes: (1) general sentiments (1614 tweets, 30.4%); (2) delaying or forgoing dental care (1190 tweets, 22.4%); (3) payment strategies (1019 tweets, 19.2%); (4) insurance (767 tweets, 14.4%); and (5) policy statements (724 tweets, 13.6%). Geographic distributions of the tweets established California, Texas, Florida, and New York as the states with the most tweets. Qualitative analysis revealed barriers faced by individuals to accessing dental care, strategies taken to cope with dental pain, and public perceptions on aspects of dental care policy. The volume and thematic trends of the tweets corresponded to relevant societal events, including the COVID-19 pandemic and debates on health care policy resulting from the election of President Joseph R. Biden. CONCLUSIONS: The findings illustrate the real-time sentiment of social media users toward the cost of dental treatment and suggest shortcomings in funding that may be representative of greater systemic failures in the provision of dental care. Thus, this study provides insights for policy makers and dental professionals who strive to increase access to dental care.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".