20 Prescriptions for joy – Charitable wishes for Canadian children 2022-2024
Bibliographic record
Abstract
Abstract Background For decades, Canadian children with critical illness have been referred to the non-profit charity, Make-A-Wish Canada (MAWC), for wishes. These experiences are reported to foster joy, resilience and family bonding in face of serious health conditions, but there is limited evaluative data. While eligibility criteria exist, there may be regional or diagnostic disparities in wish referrals potentially creating a systematic disadvantage to equally deserving populations. Objectives To quantitatively analyze the characteristics and outcomes of wishes granted by MAWC to Canadian children between 2022-2024, describing demographics, wish types, time to wish completion, and regional allocation, to identify trends or inequity in referrals and/or wish granting. Design/Methods The data was retrospectively collected from wish referral forms, anonymized and entered electronically in a secure database. Data was analyzed quantitatively and home postal codes were used to categorize children by region. Wishes were sorted by type: to go (travel), to have (item), to meet (celebrity), to be (role), to give or other. Variables of interest included: primary medical diagnosis, child’s age, sex, self-reported ethnic group, and time to wish fulfillment. Results Between Jan 2022-2024, 3985 children were referred for wishes and 3400 wishes were granted. 45% of recipients were female with a mean age of 12.2 years. Of the 6% of families (n=240) who reported ethnicity, 44% were Caucasian, 22% Asian and 12% Indigenous. Half (51%) of recipients had an oncologic condition. Most recipients wished to go somewhere (69%), most frequently Disney World (38%), followed by a wish to have an item (27%). On average, wishes involved 4 (1-12) family participants and the time to wish was 1110 days from referral with significant delays post pandemic. Recipients from Canada’s four most populous cities, Toronto, Montreal, Vancouver and Calgary, made up 10% of wishes; 17% of children lived in a rural area. Wishes were granted in over 1000 cities across Canada but there may be underserved regions such as the Yukon, Northwest Territories and Nunavut where 12 wishes were granted. Conclusion With 5 wishes granted daily, this study shows a significant volume and outcome of wishes delivered by MAWC. While granted in all regions, wish recipients living in the far north and rural areas may have been under-represented, however, without precise denominator data this remains unclear. Ethnicity information was available for a small subset, limiting the generalizability. Continued prospective exploration of potential disparities in wish referral/granting and in measurable impact of wishes is indicated. Potential competing interests Jeremy Friedman is the Chair of the Medical Advisory Board of Make-a-Wish Canada. This is a volunteer position. Hema Patel is a member of the Medical Advisory Board of Make-a-Wish Canada. This is a volunteer position. Neither Dr. Friedman nor Dr. Patel receive any payment from MAWC, nor have any financial investment in this non-profit charity that is aimed at providing wishes for children with severe and life-threatening illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".