Recruiting family physicians and patients for a clinical trial: lessons learned
Bibliographic record
Abstract
BACKGROUND: The randomized controlled trial (RCT) is the most definitive tool for evaluating an intervention. However, methodological deficiencies may limit the internal or external validity of the RCT. OBJECTIVE: Our aim was to describe the tactics used and the resources required randomly to select and recruit family physicians (FPs) and their patients aged 65 and older (seniors) for a community-based cluster RCT in primary care. METHODS: We randomly selected 48 FPs in 24 urban and rural sites in Southern Ontario, and 889 of their community-dwelling seniors (approximately 20 per FP) taking five or more medications daily. To accomplish this, the principal investigator (an FP) contacted the eligible FPs. The participating FPs' office staff then generated and contacted the roster of eligible seniors, with support provided by the research staff. RESULTS: Of the 163 randomly selected FPs telephoned, 94 were ineligible and 48 (69.6%) of the remaining 69 participated. The rosters were generated with the assistance of the research staff (taking 1.5-8.0 hours) in each of the 48 practices, using electronic appointment records (n = 26), electronic billing records (n = 17), electronic medical records (n = 2) or written charts or file cards (n = 3). Of the 2078 seniors approached, 799 were ineligible and 889 (69.5%) of the remaining 1279 participated. Seniors' refusal rates among practices ranged from 4.8 to 62.3%. CONCLUSIONS: Recruitment of a representative sample and generalizability of results are possible in RCTs in primary care. Involvement of an FP in physician recruitment and clinical research nurses who provided assistance to office staff were keys to success.
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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 | 0.359 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".