20 Prescriptions for joy – Charitable wishes for Canadian children 2022-2024
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».