A conceptual framework for the public health monitoring of substance-related harms
Bibliographic record
Abstract
Executive summary: The drug toxicity crisis in Canada and elsewhere has increased the need for timely and relevant data to inform policies and programs aimed at mitigating substance-related harms. While a number of monitoring systems addressing specific components of substance use and related harms in Canada exist, they are not guided by an overarching conceptual framework. This evidence-informed policy brief describes the development of a conceptual framework for the public health monitoring of substance-related harms. The resulting framework includes four primary topic areas (risk and protective factors, substance use, health supporting systems and substance-related harms and benefits) four cross-cutting topic areas (life course, equity, substance use stigma and mental and physical health and illness) and two overarching considerations (respectful use of data and engagement). This framework can be used to organize existing activities and to identify data and monitoring gaps for further development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".