Studying physician-adolescent patient communication in community-based practices: recruitment challenges and solutions
Bibliographic record
Abstract
BACKGROUND: Practice-based studies are needed to assess how physicians communicate health messages about weight to overweight/obese adolescent patients, but successful recruitment to such studies is challenging. This paper describes challenges, solutions, and lessons learned to recruit physicians and adolescents to the Teen Communicating Health Analyzing Talk (CHAT) study, a randomized controlled trial of a communication skills intervention for primary care physicians to enhance communication about weight with overweight/obese adolescents. MATERIALS AND METHODS: A "peer-to-peer" approach was used to recruit physicians, including the use of "clinic champions" who liaised between study leaders and physicians. Consistent rapport and cooperative working relationships with physicians and clinic staff were developed and maintained. Adolescent clinic files were reviewed (HIPAA waiver) to assess eligibility. Parents could elect to opt-out for their children. To encourage enrollment, confidentiality of audio recordings was emphasized, and financial incentives were offered to all participants. RESULTS: We recruited 49 physicians and audio-recorded 391 of their overweight/obese adolescents' visits. Recruitment challenges included 1) physician reticence to participate; 2) variability in clinic operating procedures; 3) variability in adolescent accrual rates; 4) clinic open access scheduling; and 5) establishing communication with parents and adolescents. Key solutions included the use of a "clinic champion" to help recruit physicians, pro-active, consistent communication with clinic staff, and adapting calling times to reach parents and adolescents. CONCLUSION: Recruiting physicians and adolescents to audio-recorded, practice-based health communication studies can be successful. Anticipated challenges to recruiting can be met with advanced planning; however, optimal solutions to challenges evolve as recruitment progresses.
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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 | 0.149 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".