A population-based repeated cross-sectional study using administrative health data to examine the impact of the COVID-19 pandemic on mental wellness in citizens of the Métis Nation of Ontario
Bibliographic record
Abstract
ObjectiveLinking data on people who experience drug overdoses can provide new insights and opportunities for intervention. However, because public health data infrastructure in the United States (US) is often siloed, linkage activities can require incentivization and support. In 2023, we began funding 20 states to conduct overdose-related data linkage. We sought to understand baseline capacity in funded states. ApproachData linkages in this project focus on two areas: 1) linking fatal and nonfatal overdose data and 2) linking fatal or nonfatal overdose data to prescription drug monitoring program (PDMP), criminal justice, or social determinants of health (SDOH) data. We report opportunities identified during implementation regarding states’ capacity to perform the required linkages as well as needs for additional training and technical assistance. ResultsOf 20 funded states, 11 (55%) reported some previous experience linking fatal and nonfatal overdose data; 10 (50%) have linked PDMP data, while only 3 (15%) have linked SDOH data and 2 (10%) criminal justice data. Jurisdictional needs included: training on data linkage methods, including developing and validating linkage algorithms; overcoming administrative barriers to data sharing, such as data use agreements; and developing standardized approaches to characterizing events of interest in diverse data sources. States valued sharing strategies and experiences with each other. Conclusions/ImplicationsAdditional capacity building is needed for states to successfully link and utilize overdose-related data. We are actively collaborating with state partners to facilitate peer-to-peer knowledge exchange to develop successful linkage programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".