So you think you’ve designed an effective recruitment protocol?
Bibliographic record
Abstract
Background Recruiting acutely ill patients to participate in research can be challenging. This paper outlines the difficulties the first author encountered in a study and the steps she took to overcome problems with research ethics, gain access to participants and implement a recruitment protocol in multiple hospitals. It also compares these steps with literature related to recruitment. Aim To inform and inspire neophyte researchers about the need for planning and resilience when dealing with recruitment challenges in multiple hospitals. Discussion The multiple enablers and barriers to the successful implementation of a hospital-based study recruitment protocol are explored based on a neophyte researcher's optimistic assumptions about this stage of the study. Conclusions Perseverance, adequately planning for contingencies, and accepting the barriers and challenges to recruitment are essential for completing one's research study and ensuring fulfilment as a researcher. Implications for practice Healthcare students carrying out research require adequate knowledge about conducting hospital-based, patient research to inform their recruitment plan. Maximising control over recruitment, allowing for adequate time to conduct data collection, and maintaining a good work ethic will help to ensure success.
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 | 0.224 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".