An ethnographic investigation of the maternity healthcare experience of immigrants in rural and urban Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Canada is among the top immigrant-receiving nations in the world. Immigrant populations may face structural and individual barriers in the access to and navigation of healthcare services in a new country. The aims of the study were to (1) generate new understanding of the processes that perpetuate immigrant disadvantages in maternity healthcare, and (2) devise potential interventions that might improve maternity experiences and outcomes for immigrant women in Canada. METHODS: The study utilized a qualitative research approach that focused on ethnographic research design and data analysis contextualized within theories of organizational behaviour and critical realism. Data were collected over 2.5 years using focus groups and in-depth semistructured interviews with immigrant women (n = 34), healthcare providers (n = 29), and social service providers (n = 23) in a Canadian province. Purposive samples of each subgroup were generated, and recruitment and data collection - including interpretation and verification of translations - were facilitated through the hiring of community researchers and collaborations with key informants. RESULTS: The findings indicate that (a) communication difficulties, (b) lack of information, (c) lack of social support (isolation), (d) cultural beliefs, e) inadequate healthcare services, and (f) cost of medicine/services represent potential barriers to the access to and navigation of maternity services by immigrant women in Canada. Having successfully accessed and navigated services, immigrant women often face additional challenges that influence their level of satisfaction and quality of care, such as lack of understanding of the informed consent process, lack of regard by professionals for confidential patient information, short consultation times, short hospital stays, perceived discrimination/stereotyping, and culture shock. CONCLUSIONS: Although health service organizations and policies strive for universality and equality in service provision, personal and organizational barriers can limit care access, adequacy, and acceptability for immigrant women. A holistic healthcare approach must include health informational packages available in different languages/media. Health care professionals who care for diverse populations must be provided with training in cultural competence, and monitoring and evaluation programs to ameliorate personal and systemic discrimination.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".