The development of a knowledge test of depression and its treatment for patients suffering from non-psychotic depression: a psychometric assessment
Bibliographic record
Abstract
BACKGROUND: To develop and psychometrically assess a multiple choice question (MCQ) instrument to test knowledge of depression and its treatments in patients suffering from depression. METHODS: A total of 63 depressed patients and twelve psychiatric experts participated. Based on empirical evidence from an extensive review, theoretical knowledge and in consultations with experts, 27-item MCQ knowledge of depression and its treatment test was constructed. Data collected from the psychiatry experts were used to assess evidence of content validity for the instrument. RESULTS: Cronbach's alpha of the instrument was 0.68, and there was an overall 87.8% agreement (items are highly relevant) between experts about the relevance of the MCQs to test patient knowledge on depression and its treatments. There was an overall satisfactory patients' performance on the MCQs with 78.7% correct answers. Results of an item analysis indicated that most items had adequate difficulties and discriminations. CONCLUSION: There was adequate reliability and evidence for content and convergent validity for the instrument. Future research should employ a lager and more heterogeneous sample from both psychiatrist and community samples, than did the present study. Meanwhile, the present study has resulted in psychometrically tested instruments for measuring knowledge of depression and its treatment of depressed patients.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".