Elucidating Infertility in Indigenous Populations
Bibliographic record
Abstract
Objective: This study aims to examine the prevalence, contributing factors, and healthcare access disparities related to infertility among Indigenous populations in Canada, with a focus on First Nations, Métis, and Inuit communities. Design: A mixed-methods approach combining quantitative analysis of national health survey data with qualitative interviews of Indigenous individuals and healthcare providers was employed to provide a comprehensive understanding of infertility in these populations. Materials and Methods: Quantitative data were sourced from the 2009–2010 Canadian Community Health Survey, analyzing infertility prevalence among women aged 18–44. Qualitative data is collected through semi-structured interviews with Indigenous individuals and healthcare providers in Manitoba and Ontario, focusing on experiences and perceptions of infertility and healthcare access. Statistical evaluations included prevalence estimates and thematic analysis of interview transcripts. Results: Preliminary findings indicate that Indigenous women experience higher rates of infertility compared to non-Indigenous counterparts, with significant barriers to accessing fertility care. Factors such as socioeconomic disparities, geographic isolation, cultural differences, and historical trauma contribute to these disparities. Healthcare providers report challenges in addressing infertility within Indigenous communities due to a lack of culturally appropriate services and mistrust stemming from past experiences with the healthcare system. Conclusions: Infertility among Indigenous populations in Canada is a significant yet underrecognized issue, exacerbated by systemic barriers to healthcare access. Due to a fraught historical past, there has been a paucity of research in this area. Addressing these disparities requires culturally sensitive healthcare policies, increased awareness, and community-based interventions to ensure equitable fertility care for Indigenous peoples.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".