Reliability and validity of the Hospital Anxiety and Depression Scale in an emergency department in Saudi Arabia: a cross-sectional observational study
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are prevalent psychiatric comorbidities that are known to have a negative impact on a patient's general prognosis. But screening for these potential comorbidities in a hospital's accident and emergency department has seldom been undertaken, particularly in Saudi Arabia and elsewhere in the Middle East. The Hospital Anxiety and Depression Scale (HADS) has been extensively used to evaluate these psychiatric comorbidities in various clinical settings at all levels of health care services except for the accident and emergency department. This study therefore aimed to assess the reliability and validity of the HADS for anxiety and depression among patients at a hospital accident and emergency department in Saudi Arabia. METHODS: This cross-sectional observational study was conducted from January to December 2012. The participants were 257 adult patients (aged 16 years and above) who presented at the accident and emergency department of King Khalid University Hospital, Riyadh, Saudi Arabia, who met our inclusion criteria. We used an Arabic translation of the HADS. We employed factor analysis to determine the underlying factor structure of that instrument in assessing reliability and validity. RESULTS: We found the Arabic version of the HADS to be acceptable for 95% of the subjects. We used Cronbach's alpha coefficient to evaluate reliability, and it indicated a significant correlation with both the anxiety (0.73) and depression (0.77) subscales of the HADS, thereby supporting the validity of the instrument. By means of factor analysis, we obtained a two-factor solution according to the two HADS subscales (anxiety and depression), and we observed a statistically significant correlation (r = 0.57; p < 0.0001) between the two subscales. CONCLUSION: The HADS can be used effectively in an accident and emergency department as an initial screening instrument for anxiety and depression. It thus has great potential as part of integrated multidisciplinary care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".