Recruitment and Participation of Recreational Runners in a Large Epidemiological and Genetic Research Study: Retrospective Data Analysis
Bibliographic record
Abstract
BACKGROUND: With the increasing capacity for remote collection of both data and samples for medical research, a thorough assessment is needed to determine the association of population characteristics and recruitment methodologies with response rates. OBJECTIVE: The aim of this research was to assess population representativeness in a two-stage study of health and injury in recreational runners, which consisted of an epidemiological arm and genetic analysis. METHODS: The cost and success of various classical and internet-based methods were analyzed, and demographic representativeness was assessed for recruitment to the epidemiological survey, reported willingness to participate in the genetic arm of the study, actual participation, sample return, and approval for biobank storage. RESULTS: A total of 4965 valid responses were received, of which 1664 were deemed eligible for genetic analysis. Younger age showed a negative association with initial recruitment rate, expressed willingness to participate in genetic analysis, and actual participation. Additionally, female sex was associated with higher initial recruitment rates, and ethnic origin impacted willingness to participate in the genetic analysis (all P<.001). CONCLUSIONS: The sharp decline in retention through the different stages of the study in young respondents suggests the necessity to develop specific recruitment and retention strategies when investigating a young, physically active population.
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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".