Streamlining recruitment practices and providing equal access to research opportunities: An in-depth look at the benefits and challenges of implementing a Permission to Contact platform
Bibliographic record
Abstract
Are you struggling to support participant recruitment at your institution? Are you concerned that not all patients are provided equal access to participate in research opportunities that may benefit their health and quality of life? If your answer is yes, you may be interested in learning about Permission to Contact platforms.\nParticipant recruitment for clinical research is becoming increasingly challenging in academic and clinical settings and many organizations are seeking strategies to facilitate and streamline recruitment practices. The implementation of a Permission to Contact (PTC) Platform has the potential to overcome these challenges and ensure all patients are provided with equal access to research opportunities. The PTC platform asks patients in academic health care setting to give consent to be contacted for future research opportunities that may be relevant to them. Implementing such a platform can enable completion of research studies, especially for studies requiring a large number of participants. Some organizations across Canada and internationally have begun to develop and implement PTC platforms leveraging either an opt in or opt out model of consent. During this workshop, attendees will be provided with: 1) a national and international overview of existing PTC platforms; 2) a description of N2's Permission to Contact toolkit; 3) learnings from two Ontario Academic Health Science Centres, Holland Bloorview Kids Rehabilitation Hospital and Baycrest; and 4) a review of the legal and privacy implications associated with PTC platforms.
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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 | 0.150 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".