116.6: Deceased donation reform: a qualitative assessment of impact
Bibliographic record
Abstract
In April 2019, Nova Scotia, a province in Canada, passed updated legislation that included deemed consent. Nova Scotia is the first jurisdiction in all of North America to implement an opt-out model, deemed consent model, for deceased donation. Further to this, major system reforms occurred with the changes in legislation. Specific legislative reforms included mandatory referral for deceased donation, change of consent model to deemed consent, and enabling donor and recipient connection. Major system transformations included implementation of donor physicians at key regional and tertiary care centres, total revision of the potential donor audit tool and approach, development and implementation of a donor management system, and increased public and healthcare professional education. Using a qualitative assessment approach, the known impact to the provincial deceased donor system and the current impact from the legislative reforms will be clearly articulated. Key performance indicators, including referral rates, approach and consent rates, donor conversation rates, and consented and utilized donor rates will be discussed at length to demonstrate where key changes have impacted the provincial deceased donor system. To date, preliminary results have demonstrated the following: Increase in organ donation referrals by 130% Increase in tissue donation referrals by 228% Increase in actual tissue donors by 40% Although it is still early in the implementation, ongoing analysis is occurring. Nova Scotia has undertaken a complete revision to their deceased donation program. Rarely is there an ability to examine system and legislative changes over a period of time to determine where best practices have the greatest impact on deceased donation. This presentation will provide a 2-year summary of experiences and outcomes to date that will help to confirm which changes have had the largest impact to date.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".