Overcoming the Challenges Faced by Immigrant Populations While Accessing Primary Care: Potential Solution-oriented Actions Advocated by the Bangladeshi-Canadian Community
Bibliographic record
Abstract
INTRODUCTION: Immigrants continue to face significant challenges in accessing primary healthcare (PHC) that often negatively impact their health. The present research aims to capture the perspectives of immigrants to identify potential approaches to enhance PHC access for this group. METHODS: Focus group discussions (FGDs) were conducted among a sample of first-generation Bangladeshi immigrants who had experience with PHC in Canada. A total of 13 FGDs (7 among women, 6 among men) were conducted with 80 participants (women = 42, men = 38) in their preferred language, Bangla. We collected demographic information prior to each focus group and used descriptive statistics to identify the socio-demographic characteristics of participants. We applied thematic analysis to examine qualitative data to generate a list of themes of possible approaches to improve PHC access. RESULTS: The focus group findings identified different levels of approaches to improve PHC access: individual-, community-, service provider-, and policy-level. Individual-level approaches included increased self-awareness of health and wellness and personal knowledge of cultural differences in healthcare services and improved communication skills. At the community level, supports for community members to access care included health education workshops, information sessions, and different support programs (eg, carpool services for senior members). Suggested service-level approaches included providers taking necessary steps to ensure an effective doctor-patient relationship with immigrants (eg, strategies to promote cultural competencies, hiring multicultural staff). FGD participants also raised the importance of government- or policy-level solutions to ensure high quality of care (eg, increased after-hour clinics and lab/diagnostic services). CONCLUSIONS: Although barriers to immigrants accessing healthcare are well documented in the literature, solutions to address them are under-researched. To improve healthcare access, physicians, community health centers, local health agencies, and public health units should collaborate with members of immigrant communities to identify appropriate interventions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".