Evaluation of the Quick Inventory of Depressive Symptomatology - Self-Report (QIDS-SR) in a spinal cord injury population.
Bibliographic record
Abstract
Spinal cord injury (SCI) is an acute and devastating event that results in significant and permanent life changes for the individuals who are injured, as well as their families and friends. Depression has received more attention from clinicians and researchers than any other psychological issue among persons with SCI. Measurement of depression in this population has a variety of methodological issues, including inconsistent assessments used (self-report versus clinical interviews), varying definitions of depression, inclusion and exclusion of physical symptoms in the assessment process, and use of measures that do not represent DSM-IV criteria for major depressive disorder. The primary goal of this study was to evaluate the Quick Inventory of Depressive Symptomatology - Self-Report (QIDS-SR) and provide descriptive analyses of this measure with persons with SCI. Results showed that somatic symptoms were more frequently endorsed than psychological symptoms in this population. Additionally, scores on the QIDS-SR were significantly associated with a depression diagnosis in the patient's medical chart. However, QIDS-SR scores were not found to be correlated inversely with quality of life scores as predicted. The QIDS-SR was shown to have good internal consistency and convergent validity with patients with SCI. However, it failed to demonstrate construct validity. The QIDS-SR has the potential to be a valid measure with this population and further analysis of the psychometric properties with patients with SCI is warranted.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".