Immigrant to Canada, newcomer to childhood cancer: a qualitative study of challenges faced by immigrant parents
Bibliographic record
Abstract
BACKGROUND: Given the increasing numbers of immigrant families in Canada, it is imperative that healthcare providers (HCPs) understand the caregiving experiences of immigrant family caregivers. Our study aimed to explore any special challenges faced by immigrant parents of children with cancer and to identify supportive factors. METHODS: A constructivist grounded theory approach was used. Participants included 50 first generation Chinese and South Asian parents of children with cancer who were at least six months post-diagnosis. Recruitment took place at six Canadian pediatric oncology centres. Interviews were conducted in English, Cantonese, Mandarin, Urdu, Punjabi or Hindi. Analysis involved coding and the use of the constant comparison method. Interviewing continued until no new themes emerged. RESULTS: While immigrant parents described many challenges faced by any parent of a child with cancer, the context of being an immigrant made certain experiences particularly challenging. Parents described challenges in the following areas: managing caregiving demand and financial strain, accessing support from others, and interfacing with the healthcare system. Parents described receiving a range of practical, emotional, social and informational support from extended family, their workplace, other cancer families, community organizations and HCPs. CONCLUSIONS: Our study addresses an important gap in the research literature by providing practical insight into the experiences of immigrant family caregivers. Our findings may help to inform the development of pediatric oncology policies and programs in ways that respond to the unique needs and challenges of culturally and linguistically diverse families.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".