In for a penny, in for a pound: the effect of pre-engaging healthcare organizations on their subsequent participation in trials
Bibliographic record
Abstract
BACKGROUND: Participant recruitment in clinical trials is often challenging. Building partnerships with healthcare organizations during proposal development facilitates access to the community and may influence its subsequent organization participation and participant recruitment. We aimed to assess how pre-engaging directors of homecare organizations influenced organization participation in a subsequent trial. FINDINGS: Repeated cross-sectional study prior to a cluster randomized controlled trial involving 33 eligible Health and Social Services Centres (HSSCs). During proposal development, we asked eligible HSSC directors in a randomized order about their willingness to participate in our trial, if funded. In the pre-engagement phase, 23 directors were contacted until we met sample size requirements (n ≥ 16); 19 of whom wrote letters of intent. Once funded, we contacted all 33 eligible HSSC directors in a randomized order to enroll them. Of the 19 directors who provided letters of intent, 15 agreed to participate (79 %); of the four who did not provide letters, one agreed to participate (25 %); and of the ten who had not been approached in the pre-engagement phase, two agreed to participate (20 %). Fisher exact tests indicated that providing letters of intent was associated with subsequent participation (p = 0.003). CONCLUSIONS: Given that significantly more HSSCs directors who signed letters of intent followed through with study participation, pre-engagement with trial sites during proposal development appears to improve recruitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.279 | 0.581 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".