National Newspaper Portrayal of U.S. Nursing Homes: Periodic Treatment of Topic and Tone
Bibliographic record
Abstract
CONTEXT: Although observers have long highlighted the relationship of public distrust, government regulation, and media depictions of nursing-home scandals, no study has systematically analyzed the way in which nursing homes have been portrayed in the national media. This study examines how nursing homes were depicted in four leading national newspapers-the New York Times, Washington Post, Chicago Tribune, and Los Angeles Times-from 1999 to 2008. METHODS: We used keyword searches of the LexisNexis database to identify 1,704 articles pertaining to nursing homes. We then analyzed the content of each article and assessed its tone, themes, prominence, and central actor. We used basic frequencies and descriptive statistics to examine the articles' content, both cross-sectionally and over time. FINDINGS: Approximately one-third of the articles were published in 1999/2000, and a comparatively high percentage (12.4%) appeared in 2005. Most were news stories (89.8%), and about one-quarter were on the front page of the newspaper or section. Most focused on government (42.3%) or industry (39.2%) interests, with very few on residents/family (13.3%) and community (5.3%) concerns. Most were negative (45.1%) or neutral (37.0%) in tone, and very few were positive (9.6%) or mixed (8.3%). Common themes were quality (57.0%), financing (33.4%), and negligence/fraud (28.1%). Both tone and themes varied across newspapers and years. CONCLUSIONS: Overall, our findings highlight the longitudinal variation in the four widely read newspapers' framing of nursing-home coverage, regarding not only tone but also shifts in media attention from one aspect of this complex policy area to another. The predominantly negative media reports contribute to the poor public opinion of nursing homes and, in turn, of the people who live and work in them. These reports also place nursing homes at a competitive disadvantage and may pose challenges to health delivery reform, including care integration across settings.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".