Health Care Use and Barriers to Care for Chronic Inflammatory Diseases (CID) among First and Second Generation South Asian Immigrant Children and Parents in Ontario Canada
Bibliographic record
Abstract
Although immigrants are disproportionately impacted by growing chronic inflammatory disease (CIDs) rates, yet suffer barriers to access health care, little attention has been given to their primary healthcare or specialist healthcare access as it relates to complex, chronic diseases in Canada, a country with universal health care. This study aims to investigate CID health care use and barriers to care among first- and second-generation immigrant South Asian children and parents in the Greater Toronto Area, Ontario. Drawing on analysis of 24 in depth interviews with children and parents (14 children, 10 parents), the results reveal that although CIDs disproportionately affects South Asian immigrants, they encounter health system, geographic, interpersonal, and knowledge barriers to access requisite care. These barriers exist despite participants having a GP, and are compounded further by limited familial systems, culturally insensitive care, and structural inequities that in some instances make parents choose between health access or other basic needs. Although all participants recognized the importance of specialized care, only 11 participants regularly accessed specialized care, creating new schisms in CID management. The findings suggest that a multisectoral approach that address individual and structural level socio-structural drivers of health inequities are needed to create more equitable healthcare access.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".