Stability and Change in Mental Health Profiles from Middle Adulthood to Very Old Age
Bibliographic record
Abstract
OBJECTIVES: This study investigates mental health (MH) through the dual-factor model, emphasizing both well-being and ill-being. Our objectives were to (1) identify MH profiles based on this model; (2) track these profiles over time; and (3) explore socio-demographic and physical health factors associated with these profiles. METHODS: We employed Latent Transition Analysis on data from 5,561 individuals aged 39-92, using two waves from the Survey of Health, Ageing, and Retirement in Europe. Well-being was assessed via life satisfaction and quality of life, while ill-being was measured through depression and loneliness. The predictors were socio-demographic and physical health variables. RESULTS: Four distinct MH profiles emerged, each with unique levels of well-being and ill-being. Stability was more common in adaptive profiles. Physical health was key in predicting transition. CONCLUSIONS: Identifying MH profiles in old age enhances our understanding of how MH adapts with aging. This approach reveals the complexity of MH beyond traditional ill-being, underscoring the importance of well-being. CLINICAL IMPLICATIONS: • The majority of older adults maintain good MH, suggesting a need for a paradigm shift toward enhancing well-being rather than solely treating ill-being.• Effective MH interventions should integrate both well-being and ill-being assessments to offer understanding and support.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".