The Prevalence of Depression and Its Impact on Employment Earnings among Canadian Labor Force Participants
Bibliographic record
Abstract
Depression is among the largest health capital threats to Canada’s labor force population. It is unclear how depression prevalence has been behaving over time among the Canadian working-age population, and its impact on and co-occurrence with labor market outcomes, such as employment earnings. In this dissertation, I quantify trends in major depressive episode (MDE) prevalence among various segments of the Canadian labor force and determine how experiencing depression influences employment earnings trajectories over the working life. My first study employed a cross-sectional time series methodology to quantify the trend in the prevalence of annual MDEs among different segments of the Canadian labor force from 2000 to 2016. Findings suggest that prevalence of annual MDEs was stable between 2000 and 2016; average prevalence of annual MDEs over this period was roughly double that among those unemployed or not participating in the labor force compared to those who were employed. My second study linked annual administrative tax records to population health data to determine how experiencing a MDE influences employment earnings over the subsequent decade among working-aged Canadian men and women. Using a propensity score-matched cohort design and longitudinal multi-level modeling, results suggested a crude, cumulative ten-year earnings loss of ~$71,000 CAD for working-aged women and ~$115,000 for working-aged men who experienced a MDE. My last study employed parallel latent growth modeling to quantify the number of concurrent trajectories of depression-related mental health and employment earnings over a 19-year period. The study examined an American cohort entering the labor force in 1997, followed until 2017. Four latent classes were uncovered: one class with stable poor mental health and the lowest earnings (~$32,000 USD in 2017), and three classes with stable positive mental health and earnings ranging from ~$39,500 to $196,000 in 2017. Being a woman, Black or Hispanic, poor adolescent socioeconomic status, adolescent marijuana use, and overweight body weight in 1997 was associated with higher odds of belonging to the poor mental health, lowest earnings class. Taken together, this dissertation highlights the persistent nature of depression prevalence and its longstanding impact on earnings among labor force participants over the past 20 years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".