Life Course Perspectives on the Epidemiology of Depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Life course epidemiology seeks to understand how determinants of health and disease interact across the span of a human life, and has made significant contributions to understanding etiological mechanisms in many chronic diseases, including schizophrenia. The life course approach is ideal for understanding depression: causation in depression appears to be multifactorial, including interactions between genes and stressful events, or between early life trauma and later stress in life; timing of onset and remission of depression varies widely, indicating differing trajectories of symptoms over long periods of time, with possible differing causes and differing outcomes; and early life events and development appear to be important risk factors for depression, including exposure to acute and chronic stress in the first years of life. To better understand etiology and outcome of depression, future research must move beyond basic epidemiologic techniques that link specific exposures to specific outcomes and embrace life course principles and methods. Time-sensitive modelling techniques that are able to incorporate multiple interacting factors across long periods of time, such as structural equation models, will be critical in understanding the complexity of causal and influencing factors from early development to the end stages of life. Using these models to identify key pathways that influence trajectories of depression across the life course will help guide prevention and intervention.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it