Exploring life engagement from the perspective of patients with major depressive disorder: a study using patient interviews
Bibliographic record
Abstract
Abstract Background Patient-reported outcomes can measure health aspects that are meaningful to patients, such as ‘life engagement’ in major depressive disorder (MDD). Expert psychiatrists recently identified ten items from the Inventory of Depressive Symptomatology Self-Report (IDS-SR) that can be used to measure patient life engagement. This study aimed to explore the concept of patient life engagement and provide support for the IDS-SR10 Life Engagement subscale from the patient perspective. Methods Semi-structured video interviews were conducted with adults with MDD in the United States. Patients were asked if they ever felt engaged with life, and how this affected their feelings, activities, socializing, and thoughts. Then, patients discussed the ten expert-selected IDS-SR items, and rated the relevance of all 30 items to patient life engagement on a 4-point scale. Results Patients (N = 20) understood the ‘engaged with life’ concept and could provide examples from their own lives, such as increased energy/motivation (100%), being more social/spending time with others (85%), being more communicative (80%), and having better mood (75%). Nineteen patients (95%) indicated that all ten IDS-SR10 Life Engagement items were relevant to patient life engagement, and nine of the ten items had a mean score ≥ 3 (moderately relevant). Four additional items (all relating to mood) also scored ≥ 3. Conclusions Patients found the concept of life engagement to be important and relatable, and confirmed the IDS-SR10 captures the defining non-mood-related aspects of patient life engagement. This research supports the relevance of patient life engagement as a potential clinical outcome beyond core mood symptoms, and the use of the IDS-SR10 Life Engagement subscale in patient-oriented research.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".