Loss of Life and Labour Productivity: The Canadian Opioid Crisis
Bibliographic record
Abstract
The purpose of this study is to measure and quantify the losses in labour productivity due to the Canadian opioid crisis. Since 2016, over 15,393 Canadians have lost their lives due to opioid overdose. It is estimated that 10,775 of these overdose victims were employed in the 5 years prior to their death. This study applies public data to a human capital (HC) model to estimate the total lost productivity to the Canadian economy. The HC model mathematically projects forward the future economic output of an individual overdose victim given their occupation and age (at time of death) until retirement. The total estimated productivity loss is at least $5.71 billion dollars. Given this, the opioid crisis has affected a whole working cross-section of society causing irreversible damage to the Canadian economy in addition to an immeasurable human cost. A multidisciplinary review of the literature regarding opioid use disorder was also undertaken to enhance understanding into the nature of the Canadian opioid crisis in relation to premature deaths and the subsequent losses in labor productivity.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".