A “Patient Preference” Model of Recruitment for Research from Primary-Care-Based Memory Clinics: A Promising New Approach
Bibliographic record
Abstract
Recruiting persons with dementia for clinical trials can be challenging. Building on a guide initially developed to assist primary-care-based memory clinics in their efforts to support research, a key stakeholder working group meeting was held to develop a standardized research recruitment process, with input from patients, care partners, researchers, and clinicians. Discussions in this half-day facilitated meeting focused on the wishes and needs of patients and care partners, policy and procedures for researchers, information provided to patients, and considerations for memory clinics. Patients and care partners valued the opportunity to contribute to science and provided important insights on how to best facilitate recruitment. Discussions regarding proposed processes and procedures for research recruitment highlighted the need for a new, patient-driven approach. Accordingly, a key stakeholder co-designed "Memory Clinic Research Match" program was developed that has the potential to overcome existing barriers and to increase recruitment for dementia-related research.
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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 | 0.315 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".