183 Challenges and levers influencing clinical setting recruitment for a shared decision-making stepped wedge cluster randomized trial
Bibliographic record
Abstract
Introduction Our aim is to describe the challenges and levers to recruitment for a shared decision- making (SDM) stepped wedge cluster randomized trial (SWcRT). Methods Informed by the extension of the Consolidated Standards of Reporting Trials for SWcRT, we performed sites recruitment for a SWcRT aiming at scaling up SDM for prenatal screening of trisomy in Quebec. The intervention was a set of scaling up strategies. From Quebec government websites, we compiled a list of sites potentially offering prenatal services. For data collection, we gathered recruitment meeting minutes and feedbacks from contacted eligible sites. Guided by a conceptual framework including factors influencing the recruitment such us participants characteristics, awareness and acceptance or refusal factors, we conducted a thematic analysis of gathered textual data to identify challenges and levers to the recruitment. Results Out of 477 identified potentially eligible sites, 336 were contacted by telephone calls and email messages: out of these, 74 did not respond and 50 were not eligible. In the remaining 212, although we identified spokespersons, 115 did not respond and 65 refused to participate leaving 32 participating sites: six (18.8%) university family medicine groups, eight (25%) hospitals, eight (25%) midwives’ clinics and 10 (31.2%) obstetrics and gynecology clinics. Main challenges to recruitment were delayed response, no response, heterogeneity of the sites’ culture, their fear of the potential burden from the SWcRT and local approval process. Identified levers were follow-up contacts, effective SWcRT communication, sharing the SDM positive outcomes and adaptation of the SWcRT to sites’ context. Discussion Our findings explain why some SDM SWcRT may not meet their recruitment targets. Conclusion Allocating enough time and resources to the recruitment process is imperative. Moreover, flexibility, adapting the SWcRT to sites’ context and creating a relationship of trust with sites are assets that facilitate their recruitment.
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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 | 0.517 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".