The Relationship between the Use of Social Networks and the Health Literacy of Ilam Public Library Users in 2018
Bibliographic record
Abstract
Background and Aim: Due to the impact of health literacy on how people decide about their health, such literacy is considered as one of the important issues in improving community health. The purpose of this study was to determine the relationship between the use of social networks and the health literacy of Ilam public library users. Materials and Methods: The present study is a practical research conducted based on descriptive-survey method in 2018. Sample size was selected by stratified random sampling. Besides, a three-part questionnaire was applied for data collection. Moreover, SPSS software was used for data analysis: Aanalysis of the mean, standard deviation and correlation coefficient. Results: According to the average scores, public library users have had good results in health literacy components, with the aim of understanding, assessment, reading, decision making, and access to health information. Also, there was a significant relationship between the use of social networks and userschr('39') health literacy; in other words, if there was an increase in the use of social networks, health literacy level would increase. Conclusion: Since social networks have been instrumental in enhancing the health literacy of public library users as a source of health and sanitary information, appropriate social networking can be provided to share experiences and increase the level of userschr('39') health literacy by taking into account the security of userschr('39') information and the accuracy of the given information, thereby providing new opportunities and conditions for users, doctors, patients, and planners of this domain.
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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 | 0.004 |
| 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 | 0.001 |
| 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".