Recreational Cannabis Usage Among Young Adults Living with Diabetes: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Canada is the second country in the world to legalize recreational cannabis (marijuana). Young adults are the highest users of cannabis. Recreational cannabis use may increase anxiety, depression, breathing problems, and weight in young adults in general and further result in poor control of sugar levels, increased consumption of carbohydrates, and poor self-management in young adults living with diabetes. There is limited research on the influence of recreational cannabis usage on young adults living with diabetes. PURPOSE: To explore the frequency of recreational cannabis usage, self-management behaviors and experiences of cannabis-related adverse events, to identify the factors encouraging recreational cannabis usage, and to determine the influence of recreational cannabis usage on self-reported health among young adults living with diabetes. METHODS: A sequential explanatory mixed method design will be used. The first phase will be a cross-sectional online survey in which quantitative data will be collected on the demographic and health/clinical characteristics, cannabis use patterns, and diabetes self-management of young adults living with diabetes who use cannabis. The second phase will involve the collection of qualitative data through telephone, semi-structured, one-on-one interviews. Potential participants will be recruited through study flyers, posters, and clinicians from two outpatient diabetes clinics in the province of Ontario, Canada. PLANNED OUTCOMES: The findings from this study may provide useful information regarding the effects of recreational cannabis consumption on young adults living with diabetes. This, in turn, may provide guidance to healthcare providers on how to counsel, assist, educate, and support diabetes-related management through strategies and interventions for this patient population, contributing to patient health and safety.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".