Access to Opioid Agonist Treatment during COVID-19 Public Transport Disruptions
Bibliographic record
Abstract
Public transport disruptions caused by the COVID-19 pandemic had wide-ranging impacts on the ability of individuals to access health care. Individuals with opioid use disorder represent an especially vulnerable population due to the necessity of frequent, supervised doses of opioid agonists. Focused on Toronto, a major Canadian city suffering from the opioid epidemic, this analysis uses novel realistic routing methodologies to quantify how travel times to individuals' nearest clinics changed due to public transport disruptions from 2019 to 2020. This analysis uses entirely open-source data sources to estimate the vulnerable populations that were impacted by the largest transport disruptions in the city's history. Individuals seeking opioid agonist treatment face very constrained windows of access due to the need to manage work and other essential activities. As even small changes to travel times can lead to missed appointments and heighten the chances of overdose and death, understanding the distribution of those most impacted can help inform future policy measures to ensure adequate access to care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".