Best Practices for Recruiting and Retaining Fathers in Parenting Research: Insights from Fathering Researchers
Bibliographic record
Abstract
Objective: Significant progress has been made in studying and recognizing fathers' contributions to children's development. However, fathers remain underrepresented in parenting research relative to mothers. Logistical challenges are often cited as barriers to the successful recruitment, participation, and retention of fathers. The current study sought to better understand successful strategies to overcome barriers and include fathers in parenting research. Design: = 30) to identify (1) efficient practices for recruiting fathers and (2) effective strategies for retaining fathers in longitudinal research. We used thematic analysis to generate major and minor themes. Results: Six themes emerged, four related to successful recruitment practices (the composition of the research team; the location/method of recruitment; recruiting fathers, not parents; and being flexible with testing) and two related to challenges of recruiting fathers (mothers acting as gate-closers; the restrictions applied by some institutional research ethics boards). The analysis identified two successful retention practices: developing rapport with participating fathers and maintaining contact between waves of data collection. Conclusions: These themes provide insights into how to include fathers in parenting research successfully. Recommendations for conducting father-inclusive research are presented as well as best practices for including fathers in research.
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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 | 0.144 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".