A Qualitative Assessment of Factors in the Uptake of Midwifery Among Diverse Populations in Thunder Bay, Ontario
Bibliographic record
Abstract
Introduction: Although the uptake of midwifery in Thunder Bay, Ontario, is above the provincial average, it is well below the World Health Organization–suggested level. Midwifery is especially underutilized by Indigenous women and by recent immigrant, refugee, and asylum-seeking women. Objective: To explore factors shaping birth-attendant choices and decisions of diverse women in northwestern Ontario. Methods: Drawing on data from a larger pilot study, this paper discusses factors in choosing midwifery for Indigenous, Euro-Canadian, and visible-minority (VM) women in Thunder Bay. Using in-depth interviews, we explored where the women obtained information regarding birth-attendant options, how and why they chose their caregiver, and their perceptions of the quality of their maternal care experiences. Results: Participating women’s birth-attendant choices and experiences were influenced by (1) health care provider and/or social network awareness of (and attitudes towards) midwifery; (2) personal knowledge; (3) access to midwifery; and (4) understanding of the pregnancy as being on a medical risk continuum or as a normal, healthy process. Additional influences for VM women include a lack of formally educated midwives and social status gained through having a physician in their country of origin. Additional influences for Indigenous women were the effects of colonization, discrimination, and racism. Conclusion: Women (particularly VM and Indigenous women), their families, and health care providers in northwestern Ontario need more and easier access to midwives and to knowledge about their services and scope of practice. Also, increased focus on antiracist and culturally safe practice in health care provider curricula would help improve care for Indigenous women. This article has been peer reviewed.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".