Reliability and Validity of the Chinese Version of the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) in Patients with Major Depressive Disorder—a Cross-Cultural Adaptation Study
Bibliographic record
Abstract
Background: Assessing adverse medication reactions can play a vital role in maximizing therapeutic outcomes by promoting the adherence and minimizing the cost of medication therapy in patients with major depressive disorder (MDD). Selecting a simple clinical tool that helps physicians assess and treat patients more effectively is necessary. The Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale has already been proven to be an effective measurement. This study aimed to identify the reliability and validity of the Chinese version of the FIBSER in MDD. Methods: Patients who had been diagnosed with MDD according to the Diagnostic and Statistical Manual for Mental Disorders - Fifth Edition (DSM-5) were enrolled (n=105). The depressive symptoms and adverse medication reactions were assessed by using the Hamilton Depression Scale (HAMD), the Treatment Emergent Symptom Scale (TESS), and the Frequency, Intensity, and Burden of Side Effects Rating Scale (FIBSER). The psychometric analysis was conducted on the FIBSER. Results: The Cronbach's α coefficient for the FIBSER in patients with MDD ranged from 0.872 to 0.942. After four weeks, the test-retest reliability was evaluated with the intraclass correlation coefficient (ICC) ranging from 0.335 to 0.456. The parallel validity of the FIBSER was examined using Pearson's correlation analysis, and r values ranged from 0.694 to 0.776 (P<0.001), which indicated significantly moderate to high correlations between FIBSER and TESS. Conclusion: The Chinese Version of the FIBSER demonstrates acceptable validity and internal consistency reliability, though test-retest reliability was low in this sample of major depressive disorder 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".