Trust in Health Information Sources Among Patients With Systemic Lupus Erythematosus in the Social Networking Era: The TRUMP2-SLE Study
Bibliographic record
Abstract
OBJECTIVE: The growing use of social networking services (SNS) has affected how patients with systemic lupus erythematosus (SLE) access health information, potentially influencing their interaction with healthcare providers. This study aimed to examine patients' preferences, actual use, and trust in various health information sources, along with the factors influencing the trust among patients with SLE. METHODS: A multicenter, cross-sectional survey was conducted from June 2020 to August 2021, involving 510 Japanese adults with SLE. Participants reported their preferred and actual sources of health information, including SNS, and their level of trust in these sources. Modified Poisson regression was used to analyze factors influencing trust, including internet usage and health literacy (HL; functional, communicative, and critical). RESULTS: Most respondents (98.2%) expressed trust in doctors, whereas trust in websites and blogs (52%) and SNS (26.8%) was lower. Despite this, the internet was the most frequent initial source of health information (45.3%), encompassing medical institution websites, patient blogs, X (formerly known as Twitter), and Instagram. Longer internet usage periods were associated with a greater trust in websites and blogs and SNS. Higher functional HL was correlated with an increased trust in doctors but decreased trust in homepages/blogs and SNSs. Higher communicative HL was linked to a greater trust in doctors, websites, and blogs. CONCLUSION: Although many patients with SLE initially seek health information online, they prefer to consult rheumatologists. Internet usage duration and multidimensional HL influence trust in online sources. Healthcare providers should consider these factors when disseminating health information and engaging with patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".