Permission to Contact (PTC)—A Strategy to Enhance Patient Engagement in Translational Research
Bibliographic record
Abstract
UNLABELLED: Improving patient recruitment and consent to participate in clinical studies is an important issue. The process of consent involves three steps: patient referral for contact, the preliminary interview to determine patient interest, and the informed consent discussion. We hypothesized that putting the first step of the consent process into a 'Permission to Contact' (PTC) platform would improve patient engagement, would improve the efficiency of the other steps of the process, and would be acceptable to diverse patient groups. METHODS: To test this hypothesis, four PTC platforms were established in three types of outpatient health clinics (cancer, cardiac, maternal health) in different British Columbia health centers. Each began as a research project where clinic personnel were engaged, clinic flow processes were mapped, and a design for each PTC was derived by consensus. All patients at these clinics were asked for 'permission to be contacted for future research purposes.' Patient approach and permission response rates were assessed and operational costs were estimated. RESULTS: Overall permission rates were high for all projects, but ranged from 94% of 'cancer' patients to 80% of 'congenital heart' patients who were approached (p<0.0001). Sustainability was demonstrated by stable enrollment levels after several years, and ongoing costs averaged $25 (range $12-$39) for each 'permission' across all four platforms. CONCLUSIONS: A PTC platform is a feasible mechanism to engage patients in research programs such as biobanking. It is well supported by clinic staff and receives high engagement and acceptance from patients. Patient-approach rates vary in different clinics, likely due to both clinic and PTC process factors, but this strategy provides an efficient means of engaging patients in research and sets the stage for enhanced enrollment into translational research programs.
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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 | 0.157 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".