Validation of visual analog scales of mood and anxiety at the workplace
Bibliographic record
Abstract
The Hospital Anxiety and Depression Scale (HADS) is commonly used to detect depressive or anxious states, but its 14-item questionnaire is time-consuming. Visual analog scales (VAS) are easy to use and quick to implement. Although the VAS has been validated to assess pain and occupational stress, VAS scores for anxiety and mood have never been evaluated in the workplace. We aimed to validate the use of visual analog scales (VAS) for anxiety and mood compared to HADS in workers. A HADS self-reported questionnaire associated with VAS assessing perceived anxiety and mood on a horizontal line of 100 mm was administered to 182 workers, with a second test (retest) proposed one week later. Sociodemographic, characteristics of work, sleep, well-being, and stress were also assessed. VAS anxiety and mood correlated with the HADS sub-scores (0.70 and 0.65, respectively). The test-retest reliability was good. Optimal VAS cut-offs were ≥ 60/100 for anxiety and ≤ 60/100 for mood, to define at-risk patients. The VAS is quick to perform, easy to use, and reliable for screening depression and anxiety in occupational medicine. We recommend validated questionnaires for at-risk patients. CLINICALTRIALS: gov: NCT02596737. Available at: https://www. CLINICALTRIALS: gov/ct2/show/NCT02596737.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".