Perceived Challenges and Unmet Primary Care Access Needs among Bangladeshi Immigrant Women in Canada
Bibliographic record
Abstract
INTRODUCTION: Understanding barriers in primary health care access faced by Canadian immigrants, especially among women, is important for developing mitigation strategies. The aim of this study was to gain an in-depth understanding of perceived challenges and unmet primary health care access needs of Bangladeshi immigrant women in Canada. METHODS: In this qualitative study, we conducted 7 focus groups among a sample of 42 first-generation immigrant women on their experiences in primary health care access in their preferred language, Bangla. Descriptive analysis was used for their socio-demographic characteristics and inductive thematic analysis was applied to the qualitative data. RESULTS: The hurdles reported included long wait time at emergency service points, frustration from slow treatment process, economic losses resulting from absence at work, communication gap between physicians and immigrant patients, and transportation problem to go to the health care centers. No access to medical records for walk-in doctors, lack of urgent care, and lack of knowledge about Canadian health care systems are a few of other barriers emerged from the focus group discussions. CONCLUSIONS: The community perception about lack of primary health care resources is quite prevalent and is considered as one of the most important barriers by the grassroots community members.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".