Increasing Organ Donor Registration in a Primary Care Clinic
Bibliographic record
Abstract
Only 30% of Ontarians are registered organ donors in spite of the vast unmet need for organ donations in Ontario, Canada. The purpose of this quality improvement (QI) initiative was to increase the number of registered organ donors in a primary care practice by providing an educational fact sheet and registration form to patients in the clinic's waiting room. Three Plan-Do-Study-Act (PDSA) cycles were conducted. In the first PDSA cycle, we created an information sheet to explain the need for organ donors and the registration process. Nine patients were surveyed regarding the clarity of the information sheet, which resulted in subsequent modification of the information sheet prior to the second PDSA cycle. For the second cycle, the revised information sheet was attached to a donor registration form and distributed to 30 patients in the primary care practice over a two-week period. 23 forms were returned, in which 4 patients were already registered organ donors and 5 patients completed registration forms. In the third PDSA cycle, a more compelling graphic was used on the pamphlet. Similarly, 30 forms were distributed; 23 forms were returned, with 6 newly completed registration forms. Overall, the project increased the donor registration rate from 10.0% to 28.3%. The process allowed patients to become more knowledgeable about organ donation need and aware of the Trillium Gift of Life website. We believe that providing patients with an information pamphlet and registration form in the clinic waiting room enhanced their awareness of organ donation and facilitated registration without delay. This QI initiative represents an effective and practical study to increase donor knowledge and provide opportunities for interested individuals to become registered organ donors.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".