Receptiveness to participating in cannabis research in pregnancy: a survey study at The Ottawa Hospital
Bibliographic record
Abstract
Background: The prevalence of cannabis use among pregnant individuals in Canada is increasing. In the design of new cohort studies to evaluate the patterns and outcomes of cannabis use in pregnancy, consideration must be given to the factors influencing participation, data sharing, and contribution of biological samples. Our objective was to assess the willingness of pregnant individuals to participate in prospective research during pregnancy. Methods: We surveyed pregnant individuals receiving obstetrical care through The Ottawa Hospital in Ottawa, Canada. The survey consisted of 23 dichotomous (yes/no), multiple-choice, Likert scale, and open-ended questions. Individuals were provided with a hypothetical research scenario and asked to report on the likelihood of their participation, use and storage of personal health information and contribution of maternal and newborn samples. Individuals provided motivating and deterring factors related to research participation. Descriptive statistics included frequencies (n) and percentages (%) for categorical variables. Continuous variables were described using means and standard deviations. Results: A total of 84 survey responses were collected. The mean age of respondents was 32.6(±5.3) years. Respondents were predominantly Caucasian (79%), college/university educated (85%) with a household income of ≥$100,000 (64%). There was a high degree of willingness to participate in prospective research by sharing data and biological samples. The most commonly cited motivating and deterring factors for participating in future research were a desire to contribute to science and health information (79%) and fear of privacy invasion (17%), respectively. Conclusions: Pregnant individuals receiving care at The Ottawa Hospital are willing to participate in prospective research studies, including those related to cannabis use. Survey respondents were predominantly of higher socioeconomic status, and few individuals reported cannabis use during pregnancy. Future studies should accommodate multiple recruitment strategies and flexible study designs to encourage enrollment from and retention across diverse sociodemographic communities.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".