How one small text change in a study document can impact recruitment rates and follow-up completions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The validity and reliability of longitudinal research is highly dependent on the recruitment and retention of representative samples. Various strategies have been developed and tested for improving recruitment and follow-up rates into health-behavioural research, but few have examined the role of linguistic choices and study document readability on participation rates. This study examined the impact of one small text change, assigning an inappropriate or grade-8 reading level password for intervention access, on participation rates and attrition in an online alcohol intervention trial. METHODS: Participants were recruited into an online alcohol intervention study using Amazon's Mechanical Turk via a multi-step recruitment process which required participants to log into a study portal using a pre-assigned password. Passwords were qualitatively coded as grade-8 and/or inappropriate for use within a professional setting. Separate logistic regressions examined which demographic, clinical characteristics, and password categorizations were most strongly associated with recruitment rates and follow-up completions. RESULTS: = 0.005). CONCLUSIONS: Altogether, these findings suggest that some linguistic choices may play an important role in recruitment, while others, such as readability, may have longer-term effects on follow-up rates and attrition. Possible explanations for the findings, as well as, sample selection biases during recruitment and follow-up are discussed. Limitations of the study are stated and recommendations for researchers are provided. TRIAL REGISTRATION: ClinicalTrials.gov NCT02977026. Registered 27 Nov 2016.
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.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it