Impact of couple vs. individual participation in pregnancy research: A comparative analysis of participant characteristics and study retention
Bibliographic record
Abstract
PURPOSE: Attrition of participants over time poses a challenge in longitudinal research. This study aimed to explore how partner participation influenced maternal retention. METHODS: Using data from the P3 Cohort (a longitudinal pregnancy cohort), study retention was assessed at each stage of data collection up to 1 year postpartum. Participants were grouped according to their partner's level of participation in the study (participants who did not consent to the study team contacting their partners, participants whose partners were contacted but did not consent to participate, and participants whose partners actively participated). Cox proportional hazards models were used to evaluate the association between partner participation and participant attrition. RESULTS: Of 2194 eligible participants, 38.9 % did not provide consent for the study team to contact their partner, and 42.1 % of partners that were contacted agreed to participate in the cohort. Retention rates in the cohort were high (97.5 % at 1 year postpartum) but varied by partner participation. Partner participation was associated with a significantly reduced hazard of attrition (HR=0.38, 95 % CI:0.15-0.92). CONCLUSIONS: Active partner participation significantly enhances maternal participant retention. Inclusion of partners in pregnancy research may help reduce attrition and gain a more comprehensive understanding of family dynamics.
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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 | 0.061 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".