Evaluation of a Hockey Deceased Organ Donation Awareness Campaign: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: The Kidney Foundation of Canada developed a pilot campaign to educate persons attending junior hockey league games in London, Ontario, Canada, on deceased organ donation. OBJECTIVE: To evaluate the impact of a hockey campaign on the number of new organ and tissue donor registrants. DESIGN: Population-based retrospective cohort study. SETTING: Residents of London, Ontario. PATIENTS: We included 255 476 individuals eligible to register for organ donation with a London, Ontario postal code. MEASUREMENTS: We compared the number of new deceased organ donor registrants in London, Ontario, during the campaign period (March 12 to April 16, 2015) with 3 different time periods (December 30, 2014 to February 3, 2015; February 4 to March 11, 2015; April 17 to May 22, 2015). We also compared registration rates in London with 2 Ontario cities (Kitchener-Waterloo and Hamilton) matching in a 1:1 ratio on age, sex, and income quintile. METHODS: To compare registrations across time periods, we used binomial regression with an identity link function and generalized estimating equations with an independence correlation structure. We used modified Poisson regression to compare registration rates between cities. RESULTS: During the campaign period, there were slightly more registrations (1218 registered of 252 832 unregistered individuals [0.48%]) compared with an earlier time period (risk difference: 0.09%; 95% confidence interval [CI]: 0.05%-0.12%). However, there was no significant difference compared with 2 time periods immediately before and after the campaign. London had slightly more registrations during the campaign period compared with the matched city of Hamilton (1180 registered of 236 582 unregistered individuals [0.50%] vs 490 registered of 236 582 unregistered individuals [0.21%]; risk ratio: 2.41; 95% CI: 2.17-2.68). The registration rate in London did not significantly differ from Kitchener-Waterloo. LIMITATIONS: Unable to conclude whether the minor increase in deceased organ donor registration was the result of the campaign or other factors (e.g., simultaneous organ registration events, seasonality). CONCLUSIONS: Overall, a minor increase in deceased organ donor registration was observed during the hockey organ donation awareness campaign; however, the specific impact of the campaign on organ donor registration could not be determined.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".