Assessment of patient life engagement in major depressive disorder using items from the Inventory of Depressive Symptomatology Self-Report (IDS-SR)
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes can measure domains that are personally meaningful, such as life engagement, which reflects motivation, pleasure, and well-being. This study explored whether certain items from the Inventory of Depressive Symptomatology Self-Report (IDS-SR) can capture patient life engagement in major depressive disorder (MDD). METHODS: IDS-SR life engagement items were identified by a) a panel of expert psychiatrists (n = 4), b) patient interviews (n = 20), and c) a principal component analysis (PCA) to explore clustering of items. Psychometric analyses were performed on potential subscales, and a minimal clinically important difference (MCID) was estimated by anchor- and distribution-based methods. IDS-SR data were obtained from three randomized controlled trials of adjunctive brexpiprazole in MDD. RESULTS: Expert psychiatrists selected 10 items by consensus from the IDS-SR that might capture patient life engagement (Cronbach's alpha, 0.82; item-total correlations, 0.36-0.58). Patient interviews identified 13 items as moderately to very relevant to life engagement (Cronbach's alpha, 0.85; item-total correlations, 0.35-0.61). The PCA revealed a cluster that included all 10 items selected by psychiatrists and 11 items identified by patients. Expert psychiatrists intentionally distinguished life engagement and core depressive symptoms, although patient insights and the PCA indicated that these aspects of MDD are strongly linked. The 10-item IDS-SR life engagement subscale had an MCID of 3-5 points. CONCLUSIONS: Different approaches consistently identified a subset of 10 IDS-SR items that can measure life engagement in MDD, which may be suitable to group into an IDS-SR life engagement subscale.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".