Influence of Information Access on Organ Donation: A Questionnaire-Based Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Introduction Organ transplantation is the sole effective treatment for end-stage organ diseases. However, the availability of donor organs remains insufficient. This shortage is driven by several factors, with access to accurate information being the key determinant of an individual’s willingness to donate organs. Methods A cross-sectional study based on anonymous surveys conducted from January to December 2019, categorizing participants into healthcare professionals and non-healthcare individuals. Data included willingness to donate organs, reasons for refusal, age, education level, and understanding of brain death. Statistical significance was set at p<0.05. Results A total of 408 participants were included: 203 in the healthcare sector and 205 in the non-healthcare sector. Among healthcare professionals, 90% were willing to donate organs compared to 43% in the non-healthcare group (p<0.001). Non-healthcare respondents refused due to the fear of being alive during organ removal (74%), concerns about reduced emergency care (21%), and religious beliefs (5%). Despite these concerns, 88% acknowledged that organ donation saves lives and 95% recognized the gap between organ supply and demand. No significant differences in education levels were found between donors and non-donors, but healthcare professionals had a significantly better understanding of brain death (p<0.001). All respondents indicated that they would accept a donated organ, if needed. Conclusion Healthcare professionals are more inclined to be organ donors than are those outside the field. Misunderstandings among non-healthcare individuals contributed to higher refusal rates. Tailored awareness campaigns and educational programs could rectify these misconceptions, potentially improving donation rates and mitigating organ shortage crises.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".