Incorporating Children’s Lives into a Life Course Perspective on Stress and Mental Health
Bibliographic record
Abstract
Emerging themes in demography, developmental medicine, and psychiatry suggest that a comprehensive understanding of mental health across the life course requires that we incorporate the lives of children into our research. If we can learn more about the ways in which the stress process unfolds for children, we will gain important insights into the factors that influence initial set points of trajectories of mental health over the life course. This will simultaneously extend the scope of the stress process paradigm and elaborate the life course perspective on mental health. Incorporating children's lives into the sociology of mental health will also extend the intellectual influence of the discipline on sociomedical and biomedical research on mental illness. I contend that sociology's greatest promise in understanding trajectories of mental health across the life course lies in a systematic analysis of the social and social-psychological conditions of children, the stressful experiences that arise out of these conditions, and the processes that mediate and moderate the stress process in childhood. In this regard, there are three major issues that sociologists could begin to address: (1) the identification of structural and institutional factors that pattern children's exposure to stress; (2) the construction of a stress universe for children; and (3) the identification of key elements of the life course perspective that may set or alter trajectories of mental health in childhood and adolescence.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".