Caregivers of children with rare diseases: experiences and needs in a Canadian population
Bibliographic record
Abstract
Rare diseases (RDs) affect 2.5 million Canadians, often starting in childhood. Due to medical complexities and limited disease knowledge, many children with RDs are known to face a lengthy diagnostic odyssey. Previous research highlights the need for social connection to other RD families, informational resources, and psychosocial support among parents and caregivers of children with RDs. Our study aims to fill a knowledge gap by examining Canadian RD caregivers’ experiences, preferences, and informational and support-seeking behaviours of Canadian RD caregivers, and how these outcomes and are correlated with various demographic factors. We analyzed a total of 95 eligible survey responses from eligible Canadian caregivers. Over half of caregivers received a diagnosis for their child within the first year of medical investigations, and a similar proportion believed the diagnosis could have been made earlier. Overall, caregivers had positive perceptions of their interactions with their child’s genetics healthcare provider (gHCPs). These perceptions were significantly associated with caregiver income, education, and type of condition that was diagnosed. Caregivers primarily relied on internet webpages, support groups, and personal networks for informational and emotional support. However, these preferences varied based on a variety of demographic factors. Notably, caregivers’ perceived level of emotional support did not improve after obtaining a diagnosis for their child, and a minority of caregivers were offered counselling, emotional support, or recommendations of support groups/RD organizations. This study quantitatively examines Canadian RD caregivers’ experiences, including their diagnostic journey, interactions with gHCPs, and their informational and support seeking behaviours. Our findings suggest that caregivers require additional psychosocial support particularly at the time of their child’s diagnosis and in the period following obtaining their diagnosis. The varied use of informational and emotional supports based on demographic factors and factors related to the diagnosis itself underscores the need for patient-centred care by healthcare providers. Overall, the findings of this study contribute to the current understanding of RD caregiver experiences in Canada and shed insight on opportunities for improving care for members of the RD community.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".