Online Health-Seeking Behaviors and Information Needs Among Patients With Lymphoma in China: Study of Regional and Temporal Trends
Bibliographic record
Abstract
BACKGROUND: Health disparities are closely associated with socioeconomic inequalities. Although this relationship is well recognized in the context of traditional health care access, its influence on online health-seeking behaviors such as posting questions on patient forums and seeking peer responses remains poorly understood, particularly in the context of resource-limited regions. Furthermore, it is unclear what types of questions are most frequently asked online and to what extent these questions receive helpful responses. OBJECTIVE: This study aims to examine how socioeconomic status influences online health-seeking behavior by analyzing regional disparities in forum participation and their correlation with economic development. In addition, it aims to identify unmet informational needs among patients with lymphoma through large language model (LLM)-based forum thread classification and expert evaluation of forum responses by using data from the largest online blood cancer forum in China. METHODS: We analyzed over 110,000 patient-initiated forum threads posted between 2012 and 2023, covering all the provinces of mainland China. Regional trends in forum participation rates were examined and correlated with economic development, as measured by gross regional product per capita. Second, an LLM was used to classify the threads into 6 predefined topics based on their semantic content, thereby providing an overview of the topics that users cared about. Additionally, an expert manual review was conducted based on relevance, accuracy, and comprehensiveness to assess whether users' questions were adequately addressed within the forum discussions. RESULTS: Regional forum participation rates were significantly associated with levels of regional economic development (Wilcoxon rank-sum test; P<.001), with the highest participation rates in the East Coast regions. Participation rates in less-developed regions steadily increased, reflecting the growing public demand for accessible health information. LLM-based analysis revealed that most discussions centered on medical concerns such as interpreting reports and selecting treatment plans across all regions. However, only 37% (117/316) of the user questions received useful responses, underscoring persistent gaps in access to reliable information. CONCLUSIONS: To our knowledge, this study represents the most comprehensive real-world investigation to date of spontaneous online forum participation and information needs among patients with cancer. Our findings highlight the necessity for government and health care providers to implement initiatives such as artificial intelligence-driven information platforms and region-specific health education campaigns to bridge information gaps, reduce regional disparities, and improve patient outcomes across China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".