Arabic Translation, Validation and Cultural Adaptation of the 7-Item Hamilton Depression Rating Scale in Two Community Samples
Bibliographic record
Abstract
OBJECTIVES: Depression is a common mental disorder, the severity of which is frequently assessed via interview-based clinical scales such as the 7-item Hamilton Depression Rating Scale (HAMD-7). The current study aimed to translate and examine the validity of an Arabic version of the HAMD-7 scale. METHODS: This study took place between February and March 2016 in the Psychiatry Department of King Saud University, Riyadh, Saudi Arabia. The HAMD-7 scale was translated into Arabic using forward and backward translation methods. A total of 153 Arabic speakers were recruited to test the translated scale, including 57 medical students and 96 members of the general public. The Arabic version of the HAMD-7 scale was completed by trained investigators during face-to-face interviews with the participants. In order to assess convergent validity, participants also completed an Arabic version of the self-assessed Patient Health Questionnaire-9 (PHQ-9) scale. Subsequently, the test-retest reliability of the translated HAMD-7 scale was evaluated two weeks later during a second interview. RESULTS: <0.001). With regards to internal consistency, the Cronbach's α values ranged between 0.607-0.756. CONCLUSION: The Arabic HAMD-7 scale was found to be reliable and valid among two samples of Arabic speakers in Saudi Arabia. However, further research among Arab-speaking patients diagnosed with depression is needed in order to establish its usefulness in assessing the severity of depressive symptoms.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".