Health Communication About Hospice Care in Chinese Media: Digital Topic Modeling Study
Bibliographic record
Abstract
BACKGROUND: Hospice care, a type of end-of-life care provided for dying patients and their families, has been rooted in China since the 1980s. It can improve receivers' quality of life as well as ease their economic burden. The Chinese mass media have continued to actively dispel misconceptions surrounding hospice care and deliver the latest information to citizens. OBJECTIVE: This study aims to retrieve and analyze news reports on hospice care in order to gain insight into whether any differences existed in heath information delivered over time and to evaluate the role of mass media in health communication in recent years. METHODS: We searched the Huike (WiseSearch) news database for relevant news reports from Chinese mass media released between 2014 and 2019. We defined two time periods for this study: (1) January 1, 2014, to December 31, 2016, and (2) January 1, 2017, to December 31, 2019. The data cleaning process was completed using Python. We determined appropriate topic numbers for these two periods based on the coherence score and applied latent Dirichlet allocation topic modeling. Keywords for each topic and corresponding topics' names were then generated. The topics were plotted into different circles, and their distances on the 2D plane was represented by multidimensional scaling. RESULTS: After removing duplicated and irrelevant news articles, we obtained a total of 2227 articles. We chose 8 as the suitable topic number for both study periods and generated topic names and associated keywords. The top 3 most reported topics in the first period were patient treatment, hospice care stories, and development of health care services and health insurance, accounting for 18.68% (178/953), 16.58% (158/953), and 14.17% (135/953) of the collected reports, respectively. The top 3 most reported topics in the second period were hospice care stories, patient treatment, and development of health care services, accounting for 15.62% (199/953), 15.38% (15.38/953), and 14.27% (182/953), respectively. CONCLUSIONS: Topic modeling of news reports gives us a better understanding of the patterns of health communication about hospice care by mass media. Chinese mass media frequently reported on hospice care in April of every year on account of a traditional Chinese festival. Moreover, an increase in coverage was observed in the second period. The two periods shared 6 similar topics, of which patient treatment outstrips hospice care stories was the most reported topic in the second period, implying the humanistic spirit behind the reports. Based on the findings of this study, we suggest stakeholders cooperate with the mass media when planning to update policies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".