NORMAN VE SKINNER’IN E-SAĞLIK OKURYAZARLIĞI ÖLÇEĞİNİN KÜLTÜREL UYARLAMASI İÇİN GEÇERLİLİK VE GÜVENİLİRLİK ÇALIŞMASI
Bibliographic record
Abstract
This study examined the cultural adaptation of an electronic health literacy survey for Turkish society, developed by Cameron D. Norman and Harvey A. Skinner from University of Toronto, and applied validity and reliability tests. The survey, which was developed for the information level of the users, is an important source of data. Psychological scales from many cultures are currently used in other cultures after cultural adaptation and language translation. This study is a good example of this. The translations were made by two English instructors (English–Turkish, Turkish–English) for the purpose of testing the language equivalence of the survey. Positive and significant correlations were observed between English and Turkish surveys. The Turkish survey form, for which validity and reliability analyses were made, was disseminated to 800 individuals. It has a one-dimensional structure with eight items, according to results of explanatory and confirmatory factor analysis which were used to determine item structure. According to the validity factor structure, internal consistency was found to be 0.863 and test–retest reliability was found to be 0.886. The electronic health literacy survey, which was intended to be added to the Turkish body of instruments, can be used in different studies on the importance of literacy in obtaining information on health using the internet and social media.
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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 | 0.003 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".