The Cedar Project: Using Indigenous-Specific Determinants of Health to Predict Substance use among Young Pregnant-Involved Indigenous Women In Canada
Bibliographic record
Abstract
BACKGROUND: Indigenous women in Canada have been hyper-visible in research, policy and intervention related to substance use during pregnancy; however, little is known about how the social determinants of health and substance use prior to, during, and after pregnancy intersect. The objectives of this study were to describe the social contexts of pregnant-involved young Indigenous women who use substances and to explore if an Indigenous-Specific Determinants of Health Model can predict substance use among this population. METHODS: Using descriptive statistics and hierarchical logistic regression guided by mediation analysis, the social contexts of pregnant-involved young Indigenous women who use illicit drugs' lives were explored and the Integrated Life Course and Social Determinants Model of Aboriginal Health's ability to predict heavy versus light substance use in this group was tested (N = 291). RESULTS: Important distal determinants of substance use were identified including residential school histories, as well as protective factors, such as sex abuse reporting and empirical evidence for including Indigenous-specific determinants of health as important considerations in understanding young Indigenous women's experiences with pregnancy and substance use was provided. CONCLUSIONS: This analysis provided important insight into the social contexts of women who have experiences with pregnancy as well as drug and/or alcohol use and highlighted the need to include Indigenous-specific determinants of health when examining young Indigenous women's social, political and historical contexts in relation to their experiences with pregnancy and substance use.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".