Charting well-being over adulthood into pandemic times: a longitudinal perspective
Bibliographic record
Abstract
The purpose of this study is to investigate how well-being changes over the adult life course from early adulthood in 1998 through to the COVID-19 pandemic in 2021. We identify diverse well-being trajectories over time in a cohort of British Columbians and explore the extent to which changes in well-being associated with the pandemic varied for individuals in these different trajectory groups. Specifically, we ask: what was the effect of the pandemic on the well-being of individuals with different prior well-being trajectories over adulthood and how were these effects related to personal, educational and employment factors? To address this question, we model well-being trajectories over a large span of adulthood from the age of 28 to 51 years old. We find a diversity of distinct patterns in well-being changes over adulthood. The majority experience high well-being over time, while almost one in five experiences either chronically low or drastically decreased well-being in mid-adulthood, which coincides with the pandemic. Notably, those who have completed post-secondary education are less likely to report low well-being trajectories. Those with the lowest well-being over time also report the largest negative effects of the pandemic, which illustrates the compounding effects of the pandemic on existing inequalities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".