Developing a Food and Drug Interaction Knowledge Scale for Health Care Professionals: A Validity and Reliability Study
Bibliographic record
Abstract
Objectives: Health professionals’ food-drug interaction knowledge level is related with efficiency of treatment. Given that, it’s important to measure the knowledge level. This study aims to develop a scale that can measure food-drug interaction knowledge level of health care professionals and increase awareness on this issue.Methods: A total of 200 individuals (50 from each profession: medical doctor, nurse, dietician and pharmacist) working in Ankara were selected. The scale consists of 25 items and three options for each: “True”, “False” and “I do not know”. In the evaluation, each correct answer equals to 1 (one) point, while the others (wrong and absent) equal to 0 (zero). Content validity and item analysis were conducted for the validity, and Cronbach alpha coefficient was measured. Results: Consequently, 4 items whose total correlation with the total score less than 0.15 were removed from the scale. Item difficulties in the scale vary between 0.20 and 0.96, and average item difficulty of the scale was found to be 0.61±0.18. The scale was evaluated on the basis of quarter points of 21 items. Accordingly, 25th percentile of 21 items was 5.25, 50th percentile was 10.25, and 75th percentile was 15.75. Score classification less than 5 means “low” knowledge level, between 6 and 11 “intermediate”, between 11 and 15 “good” and between 16 and 21 “very good”.Conclusions: Hereby, this scale was found to be highly valid and quite reliable to be used in order to determine the food-drug interaction knowledge levels of health care professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".