O34-1 Understanding the relationships between chronic conditions and labour market participation in canada
Bibliographic record
Abstract
This presentation will describe results from two studies examining the impact of chronic conditions and labour market participation, using two different representative population data sets in Canada. The first study examined the relationship between seven physical chronic conditions and labour market participation in Canada between 2000 and 2005 using repeated cross-sectional data from the Canadian Community Health Surveys (N = 122,106). The second study created a dynamic longitudinal cohort from the Canadian National Population Health Survey, to examine the relationship between the initial incidence of five different conditions and labour market participation, both concurrently and two years later. In the first study all conditions were associated with an increased probability of not being able to work due to health reasons. Heart disease was associated with the greatest probability of not working due to health reasons. Arthritis was associated with the largest population attributable fraction. In addition, particular combinations of chronic conditions (heart disease and diabetes; and arthritis and back pain) were associated with super-additive risks of not working. In the second study the onset of all conditions (with the exception of hypertension) was related to work loss concurrently (Time 1) and two year subsequently (Time 2). The relationship between chronic diseases and work loss at Time 2 was completely mediated through Time 1 work status. The results of the first study demonstrate that chronic conditions are associated with labour market participation limitations, but to differing extents. Using a stronger longitudinal design, the second study demonstrates that chronic disease diagnosis is associated with immediate and prolonged work loss. Strategies to keep older workers in the labour market in Canada will need to address barriers to staying at work that result from the initiation and continued presence of chronic conditions, and particular combinations of conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".