Use of a marketing plan for recruitment to a lung cancer screening study.
Bibliographic record
Abstract
1548 Background: Recruitment to clinic trials is typically poor. Among barriers to recruitment may be the limited knowledge of trialists with respect to marketing techniques. Improvements in marketing could decrease recruitment time and shorten the time to access new interventions. We hypothesized that a marketing plan would improve recruitment to a lung cancer screening study. Methods: The Pan-Canadian Early Detection of Lung Cancer Trial recruited subjects from 8 centres to a screening study of low-dose CT scan and autofluorescence bronchoscopy. Recruitment processes were undertaken independently at each centre. One centre (M) used marketing expertise and a marketing plan, including surveying study candidates for motivators, resulting in specific newsprint advertisements. Screened trial candidates provided demographic and tobacco use data and indicated how they had heard about the study (bus, friend/family, MD, mail, newsprint, radio, TV, other). No site paid for radio or TV time. We used regression analyses to assess whether newsprint advertisements were more effective for recruitment at site M compared with all other sites. Results: From 2008 to 2010, 7059 candidates contacted all centres for eligibility screening, including 779 at centre M. Overall, 50.2% were female; median age was 59 yrs. Compared with other centres, candidates at centre M had less education (p < 0.001), a higher median 3-year lung cancer risk (2.3 vs 2.0%, p < 0.001), but were more likely to have learned of the study by newsprint (58.8 vs 53.3%, chi-squared p = 0.004), and were more likely to be recruited (44.0 vs 34.9%, p < 0.001). It was more likely that newsprint was the driver for screening contact among candidates with higher education level (OR 1.05/level), higher age (OR 1.03 / yr) and contact at site M (OR 1.31) (all < 0.001). Recruitment after eligibility screening was higher when newsprint was the driver for contact on univariable but not multivariable analysis. Conclusions: The effectiveness of newsprint advertising in motivating study contact may be improved by the formal use of marketing expertise. Newsprint advertising may improve the likelihood of recruitment after study screening, possibly through improved initial self-screening by the candidate. Clinical trial information: NCT00751660.
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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 | 0.121 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.025 |
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".