Cyberchondria levels and relationship with health literacy in patients who visited to the family medicine outpatient clinic
Bibliographic record
Abstract
Background: With the widespread use of the internet, individuals increasingly seek health-related information online. While this can enhance health knowledge and decision-making, it may also lead to excessive and anxiety-driven searches, known as cyberchondria. It is still a new and unknown concept that reduces functionality and quality of life, harms the patient-physician relationship, and has become a great burden on the economy. Improving health literacy may play a key role in mitigating its effects. Methods: This cross-sectional descriptive study included 341 participants who visited a family medicine outpatient clinic between March 28 and June 30, 2022. Data were collected using a sociodemographic form, the cyberchondria severity scale, and the Turkey health literacy scale. Results: Of the participants, 41.3% were male and 58.7% female, with a mean age of 39.3±13.2 years. The mean cyberchondria score was 69.8±15.7, and the mean health literacy (TSOY-32) score was 31.4±8.1, indicating a problematic-limited level. Cyberchondria was negatively correlated with health literacy, number of chronic diseases, and presence of hypertension, and positively correlated with the number of online health information sources used. Higher scores were observed among those using the internet, friends/neighbors, Google, social media, and forums for health information. Conclusions: Our findings highlighted that cyberchondria was negatively associated with health literacy and chronic disease. Targeted strategies to enhance health literacy, along with promoting the responsible use of online health information, may contribute to the prevention and management of cyberchondria.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".