Factors associated with research participation in a large primary care practice-based pediatric cohort: Results from the TARGet Kids! longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: All longitudinal cohort studies strive for high participant retention, although attrition is common. Understanding determinants of attrition is important to inform and develop targeted strategies to improve study participation. We aimed to identify factors associated with research participation in a large children's primary care cohort study. METHODS: In this longitudinal cohort study between 2008 and 2020, all children who participated in the Applied Research Group for Kids (TARGet Kids!) were included. TARGet Kids! is a large primary care practice-based pediatric research network in Canada with ongoing data collection at well-child visits. Several sociodemographic, health, and study design factors were examined for their associations with research participation. The primary outcome was attendance of eligible research follow-up visits. The secondary outcome was time to withdrawal from the TARGet Kids! study. Generalized linear mixed effects models and Cox proportional hazard models were fitted. We have engaged parent partners in all stages of this study. RESULTS: A total 10,412 children with 62,655 total eligible research follow-up visits were included. Mean age at enrolment was 22 months, 52% were male, and 52% had mothers of European ethnicity. 68.4% of the participants attended at least 1 research follow-up visit. Since 2008, 6.4% of the participants have submitted a withdrawal request. Key factors associated with research participation included child age, ethnicity, maternal age, maternal education level, family income, parental employment, child diagnosis of chronic health conditions, certain study sites, and missingness in questionnaire data. CONCLUSIONS: Socioeconomic status, demographic factors, chronic conditions, and missingness in questionnaire data were associated with research participation in this large primary care practice-based cohort study of children. Results from this analysis and input from our parent partners suggested that retention strategies could include continued parent engagement, creating brand identity and communication tools, using multiple languages and avoiding redundancy in the questionnaires.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".