Giving birth in a good way when it must take place away from home: Participatory research into visions of Inuit families and their Montreal‐based medical providers
Bibliographic record
Abstract
BACKGROUND: Transferring pregnant women out of their communities for childbirth continues to affect Inuit women living in Nunavik-Inuit territory in Northern Quebec. With estimates of maternal evacuation rates in the region between 14% and 33%, we examine how to support culturally safe birth for Inuit families when birth must take place away from home. METHODS: A participatory research approach explored perceptions of Inuit families and their perinatal healthcare providers in Montreal for culturally safe birth, or "birth in a good way" in the context of evacuation, using fuzzy cognitive mapping. We used thematic analysis, fuzzy transitive closure, and an application of Harris' discourse analysis to analyze the maps and synthesize the findings into policy and practice recommendations. RESULTS: Eighteen maps authored by 8 Inuit and 24 service providers in Montreal generated 17 recommendations related to culturally safe birth in the context of evacuation. Family presence, financial assistance, patient and family engagement, and staff training featured prominently in participant visions. Participants also highlighted the need for culturally adapted services, with provision of traditional foods and the presence of Inuit perinatal care providers. Stakeholder engagement in the research resulted in dissemination of the findings to Inuit national organizations and implementation of several immediate improvements in the cultural safety of flyout births to Montreal. CONCLUSIONS: The findings point toward the need for culturally adapted, family-centered, and Inuit-led services to support birth that is as culturally safe as possible when evacuation is indicated. Application of these recommendations has the potential to benefit Inuit maternal, infant, and family wellness.
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 | 0.004 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".