Recruitment of Healthcare Providers into Research Studies
Bibliographic record
Abstract
Recruitment of a sufficient number of healthcare providers (HCPs), such as nurses and nurse practitioners (NPs), as participants is essential to generate high quality research to address issues significant for clinical practice. Often the recruitment process reported in research studies is very brief and does not capture the reality of the challenges of obtaining an adequate sample. This manuscript describes the challenges that we experienced in trying to recruit a sufficient number of HCPs, specifically NPs, into a randomized controlled trial. Based on our experience, as well as a review of the literature on recruiting HCPs, we share recommendations for researchers trying to recruit busy professionals as participants. Key findings were not just about reaching the target participants, but actually using strategies to stimulate their interest and persuading them to be involved from the beginning. Important things to consider for successful recruitment are making an effort to meet with professionals face-to-face and building relationships with administrators and other staff within organizations. Other lessons learned were to ensure to allot extra time for recruitment to allow for unanticipated challenges and to utilize multimodal strategies simultaneously to ensure a more timely execution of the recruitment process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".