Poster Introductions I--Productivity Losses of Chronic Diseases Among Canadian Labour Force from 1994 to 2005: Estimate from the Nationally Representative Samples
Bibliographic record
Abstract
Objective: This study estimates the productivity losses from different chronic disorders (e.g., heart conditions, diabetes, cancer) and some measures of risk factors (smoking, drinking) in two period of time, 1994 and 2005, among Canadian labour force.\nMethods: Using the data from the National Population Health Survey 1994 and Canadian Community Health Survey2005, the probability of having disability days, number of disability days, and income losses have been estimated and compared in years 1994 and 2005. In each year, a two-part model is used to estimate the impact of DM and other chronic disorders on labour market outcomes. Part one uses logistic regression to estimate the impact of risk factors and chronic diseases on the probability of having any disability day; part two uses log-transformed OLS regression with smearing correction to estimate the impact of each risk factor and chronic disorder on number of the disability days.\nResults: Over the past decade, the prevalence of most of the chronic disorders (e.g., diabetes, Depression, and obesity) have been increased. However, the prevalence of smoking has been decreased, and the number of regular drinkers and physical exercise has been increased. The overall trend of disability days has been increased insignificantly, for women and men.\nConclusions: The results are of use to policy makers and health service researchers interested in identifying and quantifying chronic-related productivity losses using econometric modeling.\nFarah Farahati received her PhD. in applied microeconomics from the Department of Economics, Northern Illinois University in May 2001. Following the completion of her doctoral studies, she has worked and collaborated in many academic and governmental agencies projects, such as Centers for Mental Health care Research, University of Arkansas for Medical Sciences, the Center for Program for Assessment of Technology and Health (PATH), McMaster University, the Canadian Agency for Drugs and Technologies in Health (CADTH) and most recently at the Toronto Health Economics Technology Assessment Collaborative (THETA) and Walkereconomics, inc. Farah's work experience includes applying econometric analysis and outcomes research methodologies to the health care services issues such as decision analytic modeling, cost-effectiveness analysis, and budget impact, direct and indirect burden of illness studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".