Modeling Heterogeneity in the Long-Term Trajectories of Individuals’ Well-Being
Bibliographic record
Abstract
= 41 years) surveyed annually over 13 years, we identified latent trajectories for belongingness, social support, self-esteem, and life satisfaction. Through a group-based trajectory modeling approach, we found five trajectory groups: low (3%-5%), moderate (11%-17%), moderate-high (29%-32%), high (35%-45%), and very high (11%-20%) well-being. While most individuals showed minimal changes, those with initially low well-being experienced the greatest change, in the direction of decreasing well-being over time. Individuals with higher education were more likely to follow higher well-being trajectories. Similarly, women were more likely to follow higher well-being trajectories, except for self-esteem, where men tended to score higher over time. Lastly, age and ethnicity demonstrated more complex patterns. These findings highlight the importance of acknowledging long-term heterogeneity in well-being trajectories and emphasize the need for targeted preventive mental health interventions, particularly for individuals who begin with lower well-being levels.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".