Central Recruitment: A process for engaging and recruiting individuals with spinal cord injury/disease in research at Toronto Rehabilitation Institute
Bibliographic record
Abstract
CONTEXT: Insufficient recruitment is a barrier to research and limits statistical power. We describe an initiative aimed to streamline recruitment and consent processes for inpatients with spinal cord injury or disease (SCI/D) via implementation of a Central Recruitment (CR) process. The CR process adhered to ethical standards, reduced participant burden, and maximized research participation. METHODS: In this CR process, the inpatient's nurse affirmed suitability for research approach based on fluency, cognition and health stability. A patient research liaison (PRL) was the sole contact for information regarding the research process, and introduced ongoing studies, screened for eligibility, and completed the consent process(es). RESULTS: Over five and a half years, 1,561 inpatients with SCI/D were screened for eligibility upon admission, of whom 80% (1256/1561) were deemed suitable for the PRL approach. Of those suitable for the CR process, 80% (1001/1256) agreed to discuss current research opportunities, 46% (235/516) consented to participate in one or more studies, and 86% (856/1001) agreed to future research contact. CONCLUSION: This process adhered to ethical procedures and reduced the burden of having multiple researchers approach each individual inpatient regarding research participation, with high consent rates for low-risk studies. Future evaluation of the process scalability is underway.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".