Assessing coronavirus disease 2019 pandemic impacts on the health of people who inject drugs using a novel data sharing model
Bibliographic record
Abstract
OBJECTIVE: Using an innovative data sharing model, we assessed the impacts of the COVID-19 pandemic on the health of people who inject drugs (PWID). DESIGN: The PWID Data Collaborative was established in 2021 to promote data sharing across PWID studies in North America. Contributing studies submitted aggregate data on 23 standardized indicators during four time periods: prepandemic (March 2019 to February 2020), early-pandemic (March 2020 to February 2021), mid-pandemic (March 2021 to February 2022), and late pandemic (March 2022 to February 2023). METHODS: We present study-specific and meta-analyzed estimates for the percentage of PWID who took medications for opioid use disorder, received substance use treatment, shared syringes or injection equipment, had a mental health condition, had been incarcerated, or had experienced houselessness. To examine change over time across indicators, we fit a random effects meta-regression model to prevalence estimates using time as a moderator. RESULTS: Thirteen studies contributed estimates to the Data Collaborative on these indicators, representing 6213 PWID interviews. We observed minimal change across prevalence of the six indicators between the prepandemic (March 2019 to February 2020) and three subsequent time periods, overall or within individual studies. Considerable heterogeneity was observed across study-specific and time-specific estimates. CONCLUSION: Limited pandemic-related change observed in indicators of PWID health is likely a result of policy and supportive service-related changes and may also reflect resilience among service providers and PWID themselves. The Data Collaborative is an unprecedented data sharing model with potential to greatly improve the quality and timeliness of data on the health of PWID.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.147 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".