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 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 | 0.165 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".