Levers and challenges to recruiting clinical settings for a shared decision-making stepped wedge cluster randomized trial
Bibliographic record
Abstract
Context: Little is known about factors influencing clinical settings (sites) recruitment for a shared decision-making (SDM) stepped wedge cluster randomized trial (SW-cRT). We aim at describing the challenges and levers to recruitment of sites for an SDM SWcRT. Study design and analysis: For a SWcRT aiming at scaling up SDM for prenatal screening of trisomy in Quebec, descriptive statistical analysis and qualitative thematic analysis were performed to have recruitment insights. Setting and dataset: From Quebec government websites, we compiled a list of sites potentially offering prenatal services. To collect data, we gathered recruitment meeting minutes and feedbacks from contacted eligible sites. Population Studied: To reach pregnant women, the trial included sites offering prenatal services. Intervention: The intervention was a set of SDM scaling up strategies including a web-based decision aid for pregnant women and an SDM training designed for professionals providing prenatal care. Outcome measures: Guided by the extension of the Consolidated Standards of Reporting Trials for SWcRT, we made a recruitment flowchart. Also, guided by a framework including factors influencing the recruitment such us awareness and acceptance or refusal factors, we conducted a thematic analysis of our textual data to identify challenges and levers to the recruitment. Results: Out of 477 identified potentially eligible sites, 336 were contacted by calls or email messages: 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, the 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. Conclusions: Our findings explain why some SDM SWcRT may not meet their recruitment targets. To mitigate this, 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 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 | 0.655 | 0.642 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".