Longitudinal examination of temporality in the association between chronic disease diagnosis and changes in work status and hours worked
Bibliographic record
Abstract
OBJECTIVES: To examine the longitudinal relationship between incidence of diagnosed chronic disease and work status and hours worked. METHODS: A dynamic cohort approach was taken to construct our study sample using the Canadian National Population Health Survey. Participant inclusion criteria included being employed and without a chronic health condition in the survey cycle prior to diagnosis, and participation in consecutive surveys following diagnosis. Each respondent was matched with up to 5 respondents without a diagnosed health condition. The direct and indirect associations between chronic disease and work status and hours worked following diagnosis were examined using probit and linear regression path models. Separate models were developed for arthritis, back problems, diabetes, hypertension and heart disease. RESULTS: We identified 799 observations with a diagnosis of arthritis, 858 with back pain, 178 with diabetes, 569 with hypertension and 163 with heart disease, which met our selection criteria. An examination of total effects at time 1 and time 2 showed that, excluding hypertension, chronic disease diagnosis was related to work loss. The time 2 effect of chronic disease diagnosis on work loss was mediated through time 1 work status. With the exception of heart disease, an incident case of chronic disease was not related to changes in work hours among observations with continuous work participation. CONCLUSIONS: Chronic disease can result in work loss following diagnosis. Research is required to understand how modifying occupational conditions may benefit employment immediately after diagnosis.
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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 | 0.004 | 0.011 |
| 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) | 0.002 | 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".