Sentiment analysis of U.S. News & World Report Best Children's Hospital urology rankings: A difference in positivity between the public and academic worlds
Bibliographic record
Abstract
INTRODUCTION: Initiated in 2009, the U.S. News & World Report (USNWR) pediatric urology rankings aim to guide patients and families towards high-quality urologic care. Despite this, the pediatric urology community remains divided, with significant debate over the rankings' accuracy, utility, and potential for misleading information. While some professionals argue for a collective opt-out from these rankings, citing these concerns, others highlight their positive impact on patient care, hospital benchmarking, and financial support. OBJECTIVE: Recognizing the lack of formal evaluation on how these rankings are viewed beyond the pediatric urology community, this research endeavors to fill the gap through sentiment analysis of public news articles and academic publications. STUDY DESIGN: We captured news articles from Google News and academic papers from Ovid Medline and Embase, focusing specifically on content related to the USNWR pediatric urology rankings from 2009 to 2023. Sentiment analysis was conducted using the Valence Aware Dictionary and Sentiment Reasoner (VADER) package on both news and academic texts, aiming to capture the overall sentiment through a compound score derived from the presence of sentiment-laden words. Sensitivity analysis was performed using TextBlob Pattern Analyzer tool. RESULTS: The analysis revealed a significant divergence in sentiment between news articles and academic literature. News articles exhibited a predominantly positive sentiment, with an average compound score of 0.681, suggesting a general approval or celebration of the rankings in the public sphere. Conversely, academic literature showed a more moderate sentiment, with an average score of 0.534, indicating a nuanced perspective that includes both positive views and critical reflections on the rankings. Sensitivity analysis confirmed this observation (Figure). DISCUSSION: This difference may reflect the distinct nature of news media and academic discourse. While news outlets may prioritize celebratory narratives that align with public interest and institutional pride, academic discussions tend to offer a balanced view that critically assesses both the merits and limitations of the rankings. This discrepancy underscores the complexity of interpreting and acting upon the rankings within the pediatric urology community. CONCLUSION: While the USNWR pediatric urology rankings are generally received positively by the public, as reflected in news media, the academic community presents a more reserved sentiment. These findings suggest the need for ongoing dialogue and research to understand the implications of these rankings fully. It also calls for a strategic approach to address the concerns and perceptions of healthcare professionals, aiming to leverage the rankings in a way that truly benefits patient care and informed decision-making.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".