Pediatric organ donation: What factors most influence parents??? donation decisions?*
Bibliographic record
Abstract
OBJECTIVE: To identify factors that influence parents' decisions when asked to donate a deceased child's organs. DESIGN: Cross-sectional design with data collection via structured telephone interviews. SETTING: One organ procurement organization in the Southeastern United States. PARTICIPANTS: Seventy-four parents (49 donors, 25 nondonors) of donor-eligible deceased children who were previously approached by coordinators from one organ procurement organization in the southeastern United States. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Multivariate analyses showed that organ donation was more likely when the parent was a registered organ donor (odds ratio [OR] = 1.4, confidence interval [CI] = 1.1, 2.7), the parent had favorable organ donation beliefs (OR = 5.5, CI = 2.7, 12.3), the parent was exposed to organ donation information before the child's death (OR = 2.6, CI = 1.7, 10.3), a member of the child's healthcare team first mentioned organ donation (OR = 1.4, CI = 1.2, 3.7), the requestor was perceived as sensitive to the family's needs (OR = 0.4, CI = 0.2, 0.7), the family had sufficient time to discuss donation (OR = 5.2, CI = 1.4, 11.6), and family members were in agreement about donation (OR = 2.8, CI = 1.3, 5.2). CONCLUSIONS: This study identifies several modifiable variables that influence the donation decision-making process for parents. Strategies to facilitate targeted organ donation education and higher consent rates are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".