Screening for Mental Health Problems among Patients with Substance Use Disorders: Preliminary Findings on the Validation of a Self-Assessment Instrument
Bibliographic record
Abstract
OBJECTIVES: We conducted a preliminary study on the validation of the Psychiatric Diagnostic Screening Questionnaire (PDSQ) among patients seeking treatment for substance use disorders (SUDs). METHOD: We assessed 76 patients with SUDs, using the PDSQ, followed by the Structured Clinical Interview for DSM-IV. Sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were calculated. RESULTS: Overall, the psychometric properties identified with the PDSQ in patients with SUDs differed from those found in psychiatric outpatient populations. The ROC curves were calculated for major depressive disorder, posttraumatic stress disorder, and panic disorder. The areas under the curves were 0.86 (95% CI, 0.77 to 0.95; P < 0.001), 0.79 (95% CI, 0.68 to 0.90; P < 0.001), and 0.66 (95% CI, 0.51 to 0.82; P = 0.05), respectively. CONCLUSION: The use of the PDSQ to screen for other psychiatric disorders in populations with SUDs is promising but requires larger validation studies to provide data on its psychometric properties and inform the choice of cut-off scores for this population.
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 | 0.015 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".