Characterization of depression subtypes and their relationships to stressor profiles among middle-aged and older adults: An analysis of the canadian longitudinal study on aging (CLSA)
Bibliographic record
Abstract
The current diagnostic criteria for depression do not sufficiently reflect its heterogeneous clinical presentations. Associations between adverse childhood experiences (ACEs), allostatic load (AL), and depression subtypes have not been extensively studied. Depression subtypes were determined based on clinical presentations, and their relationships to AL biomarkers and ACEs were elucidated in a sample of middle-aged and older adults. Participants from the Canadian Longitudinal Study on Aging who screened positive for depression were included (n = 3966). Depression subtypes, AL profiles and ACE profiles were determined with latent profile analyses, and associations between them were determined using multinomial logistic regression. Four depression subtypes were identified: positive affect, melancholic, typical, and atypical. Distinct associations between depression subtypes, stressor profiles and covariates were observed. Among the subtypes compared to positive affect, atypical subtype had the most numerous significant associations, and the subtypes had unique relationships to stressor profiles. Age, sex, smoking status, chronic conditions, marital status, and physical activity were significant covariates. The present study describes distinct associations between depression subtypes and measures of stress (objective and self-reported), as well as related factors that differentiate subtypes. The findings may inform more targeted and integrated clinical management strategies for depression in individuals exposed to multiple stressors.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".