Factors predictive of signed consent for posthumous organ donation
Bibliographic record
Abstract
CONTEXT: The shortage of organs for transplantation has led public health authorities to invest significant efforts in the promotion of organ donation. OBJECTIVE: To identify factors predictive of signed consent for posthumous organ donation by using the theory of planned behavior. PARTICIPANTS AND DESIGN: A random sample of 602 adults completed a questionnaire at baseline, and behavior was self-reported 15 months later. RESULTS: Logistic regression indicated that intention, perceived behavioral control, moral norm, and past behavior were factors predictive of consent for posthumous organ donation. Participants' perceived behavioral control, past behavior, and moral norm were also predictive of intention to sign, but attitude and perceived barriers were 2 additional determinants. Finally, anticipated regret and knowledge of persons who had made an organ donation were 2 moderators of the intention-behavior relationship. CONCLUSION: Overall, the results showed that intention is an important determinant of signing the organ donor's consent sticker and also highlighted that moral consideration and perceived difficulties could be 2 potential avenues for designing interventions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".