Deceased Organ Donation and the Nicholas Effect
Notice bibliographique
Résumé
Nicholas Green was a 7-year-old American boy who was shot in the head during an attempted carjacking while vacationing in Italy on September 29, 1994 (date of neurological death October 1, 1994). In a heroic decision, his family then consented to donate five of his organs and both corneas to other patients awaiting transplantation. This altruistic choice made front-page news, was lauded by the President of Italy, blessed by the Pope, and contributed to a threefold increase in subsequent donations nationally (1). The case exemplifies how deceased organ donation can lead to a positive cascade of secondary behaviors among others in the community, a phenomenon now called the Nicholas Effect (2). A community health survey was developed containing a brief scenario to validate the Nicholas Effect in a randomized control design (Textbox 1). The survey was formulated in two versions (defined as the standard version or modified version) that differed in only one background sentence. The standard version described a situation with no mention of deceased organ donation. The modified version contained the same sentence expanded to describe the actuality of deceased organ donation. The two versions otherwise contained identical data presented in the same format to elicit a respondent’s willingness to reschedule a medical appointment for another patient’s convenience. Adults waiting in line at a popular coffee shop were surveyed (n=468). Each participant received one version of the survey (standard or modified) by individualized randomized blinded assignment. Overall, 82 of the 238 respondents in the standard version were willing to reschedule their appointment. In contrast, 113 of the 230 respondents in the modified version were willing to reschedule their appointment. Chi-square statistical testing indicated a 15% absolute increase in willingness to reschedule caused by the Nicholas Effect (34% vs. 49%, P=0.002), mathematically equal to a number-needed-to-treat of about seven.TEXTBOXES: SCENARIO WITH EXACT WORDING OF DIFFERENT VERSIONSBy design, this effect is more likely explained as empathy toward families willing to donate rather than as a negative attitude toward families unwilling to donate. These findings are difficult to attribute to response bias, selective sampling, failures of randomization, thoughtless respondents, or other survey artifacts (3, 4). Of course, the results have limitations because a brief hypothetical scenario was developed, community members were surveyed in a relaxed convenient setting, and did not explore the full range of altruistic behaviors (5). Despite these limitations, the results demonstrate the Nicholas Effect in a scientific manner and corroborate past anecdotes in the media (6, 7). An awareness of the Nicholas Effect might be potentially helpful in addressing ongoing shortfalls in deceased organ donation (8). At present, many families are uncertain and vacillate when facing a consent decision (9). Public service announcements might wish to mention the Nicholas Effect for community education and media campaigns that promote organ donation (10). Conversely, a lack of awareness of the Nicholas Effect could imply that some families may not make a fully informed decision when they choose to decline organ donation (11). Nicholas Green was an inspiration who made an enduring contribution to the lives of hundreds of Italians as well as to our understanding of the psychology of human altruism. Donald A. Redelmeier 1,2,3,4,5 Jason D. Woodfine1,2,3 1 Department of Medicine University of Toronto Toronto, Canada 2 Evaluative Clinical Sciences Program Sunnybrook Research Institute Toronto, Canada 3 Institute for Clinical Evaluative Sciences Sunnybrook Research Institute Toronto, Canada 4 Division of General Internal Medicine Sunnybrook Health Sciences Centre Toronto, Canada 5 Center for Leading Injury Prevention Practice Education & Research Sunnybrook Health Sciences Centre Toronto, Canada ACKNOWLEDGMENTS The authors thank the following individuals for helpful comments: Craig DuHamel, David Juurlink, Christopher Kandel, Sharon May, Damon Scales, Stephen Stich, Christopher Yarnell, and Michael Young.
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 | 0,019 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,000 |
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 ».