Characterising methamphetamine use to inform health and social policies in Manitoba, Canada: a protocol for a retrospective cohort study using linked administrative data
Bibliographic record
Abstract
INTRODUCTION: Rising use of methamphetamine is causing significant public health concern in Canada. The biological and behavioural effects of methamphetamine range from wakefulness, vigour and euphoria to adverse physical health outcomes like myocardial infarction, haemorrhagic stroke, arrhythmia and seizure. It can also cause severe psychological complications such as psychosis. National survey data point to increasing rates of methamphetamine use, as well as increasing ease of access and serious methamphetamine-related harms. There is an urgent need for evidence to address knowledge gaps, provide direction to harm reduction and treatment efforts and inform health and social policies for people using methamphetamine. This protocol describes a study that aims to address this need for evidence. METHODS: The study will use linked, whole population, de-identified administrative data from the Manitoba Population Research Data Repository. The cohort will include individuals in the city of Winnipeg, Manitoba, who came into contact with the health system for reasons related to methamphetamine use from 2013 to 2021 and a comparison group matched on age, sex and geography. We will describe the cohort's sociodemographic characteristics, calculate incidence and prevalence of mental disorders associated with methamphetamine use and examine rates of health and social service use. We will evaluate the use of olanzapine pharmacotherapy in reducing adverse emergency department outcomes. In partnership with Indigenous co-investigators, outcomes will be stratified by First Nations and Métis identity. ETHICS AND DISSEMINATION: The study was approved by the University of Manitoba Health Research Ethics Board, and access datasets have been granted by all data providers. We also received approval from the First Nations Health and Social Secretariat of Manitoba's Health Information Research Governance Committee and the Manitoba Métis Federation. Dissemination will be guided by an 'Evidence 2 Action' group of public rightsholders, service providers and knowledge users who will ensure that the analyses address the critical issues.
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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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".