Barriers and Facilitators of Access to Healthcare Among Immigrants with Disabilities: A Qualitative Meta-Synthesis
Bibliographic record
Abstract
BACKGROUND: Immigrants with disabilities (IWDs) are disproportionately affected by a lack of access to healthcare services and face unique challenges compared to the general population. This qualitative meta-synthesis examines the barriers, facilitators, and lived experiences of IWDs accessing healthcare in the U.S. and Canada. METHODS: A theory-generating qualitative meta-synthesis approach was used to analyze and synthesize raw qualitative data. Using eight databases, 752 studies were retrieved, and 10 were selected and synthesized after a three-stage review. The final articles were assessed using the Critical Appraisal Skills Program (CASP) checklist, and a PRISMA flow chart was used to report on the selection process. RESULTS: The analysis identified structural barriers, including the bureaucracy and complexity of the system, healthcare costs, transportation, communication, long wait times, and a lack of integrated services. Cultural barriers included denial and trust, stigma and discrimination, awareness and language gaps, and lack of social support. Facilitators of access included support from immediate family members, community health centers, and social workers. CONCLUSIONS: The findings highlight the need for policy reforms to reduce bureaucratic hurdles, improve communication within healthcare systems, and enhance cultural competence among healthcare providers. Addressing these issues through integrated service models and targeted support can significantly improve the quality of life as a result of improved healthcare access for IWDs.
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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 | 0.051 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".