Promoting deceased organ and tissue donation registration in family physician waiting rooms (RegisterNow-1): a pragmatic stepped-wedge, cluster randomized controlled registry trial
Bibliographic record
Abstract
BACKGROUND: The shortage of available organs for life-saving transplants persists worldwide. While a majority support donating their organs or tissue when they die, many have not registered their wish to do so. When registered, next of kin are much more likely to follow-through with the decision to donate. In many countries, most people visit their family physician office each year and this setting is a promising, yet underused, site where more people could register for deceased organ donation. Our primary aim was to evaluate the effectiveness of an intervention to promote organ donation registration in family physician's offices. METHODS: We developed an intervention to address barriers and enablers to organ donation registration that involved physician office reception staff inviting patients to register on a tablet in the waiting room while they waited for their appointment. We conducted a cross-sectional stepped-wedge cluster randomized controlled registry trial to evaluate the intervention. We recruited six family physician offices in Canada. All offices began with usual care and then every two weeks, one office (randomly assigned) started the intervention until all offices delivered the intervention. The primary outcome was registration for deceased organ donation in the provincial organ registration registry, assessed within the 7 days of the physician visit. At the end of the trial, we also conducted interviews with clinic staff to assess any barriers and enablers to delivering the intervention. RESULTS: The trial involved 24,616 patient visits by 13,562 unique patients: 12,484 visits in the intervention period and 12,132 in the control period. There was no statistically significant difference in the percentage of patients registered for deceased organ donation in the intervention versus control period (48.0% vs 46.2%; absolute difference after accounting for the secular trend: 0.12%; 95% CI: - 2.30, 2.54; p=0.92). Interviews with clinic staff indicated location of the tablet within a waiting room, patient rapport, existing registration, confidence and motivation to deliver the intervention and competing priorities as barriers and enablers to delivery. CONCLUSIONS: Our intervention did not increase donor registration. Nonetheless, family physician offices may still remain a promising setting to develop and evaluate better interventions to increase organ donation registration. TRIAL REGISTRATION: NCT03213171.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".