Life course and mental health: a thematic and systematic review
Bibliographic record
Abstract
Objective: This study explored the influence of the life course on mental health by identifying key trends, seminal works, and themes in existing research. Additionally, it highlights the major discussions at the intersection of life course and mental health. Methods: Documents were extracted from the Web of Science Core Collection (WoSCC), to systematically analyze themes on mental health outcomes across the life course. The analysis was based on key bibliometric tools, including VOSviewer 1.6.11, R Studio software, and GraphPad Prism 9 to analyze the evolution and impact of scholarly contributions in this domain. Results: The accumulated body of research concerning the life course's impact on mental health, which began to emerge around 1990 displayed a consistently upward trend. Predominant contributions originate from developed nations and frequently look into the psychosocial determinants of mental health over life course. Life course and mental health studies have been extensively infused with biopsychosocial frameworks that consider the role of genetic makeup, neurodevelopment, cognition, affect, sociocultural dynamics, and interpersonal relationships. Life course theory application in mental health highlight the substantive effects of accumulated adversities, notably social determinants of health, adverse childhood experiences (ACEs), and their implications for subsequent mental health outcomes. Conclusion: The nexus of life course and mental health outcomes demands further scholarly interrogation, particularly within underserved regions, to strengthen protective mechanisms for vulnerable populations.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".