The Correlation of Online Health Information–Seeking Experience With Health-Related Quality of Life: Cross-Sectional Study Among Non–English-Speaking Female Students in a Religious Community
Bibliographic record
Abstract
BACKGROUND: Given the increasing availability of the internet, it has become a common source of health information. However, the effect of this increased access on health needs to be further studied. OBJECTIVE: This study aimed to investigate the correlation between online health information-seeking behavior and general health dimensions in a sample of high school students in Iran. METHODS: A cross-sectional study was conducted in 2019. A total of 295 female students participated in the study. The data were collected using two validated questionnaires: the e-Health Impact Questionnaire and the 36-Item Short Form Health Survey. The collected data were analyzed through descriptive statistics and Pearson correlation coefficients using SPSS version 23 (IBM Corp). RESULTS: The participants moderately used online information in their health-related decisions, and they thought that the internet helped people in health-related decision making. They also thought that the internet could be used to share health experiences with others. Participants had moderate confidence in online health information and stated that the information provided by health websites was moderately understandable and reliable and moderately encouraged and motivated them to play an active role in their health promotion. Nevertheless, the results showed that online health information-seeking experience had no significant correlation with health-related quality of life. CONCLUSIONS: This study provides insights into the effect of using internet information on the health of adolescents. It has important implications for researchers and policy makers to build appropriate policies to maximize the benefit of internet access for health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".