Recruitment strategies should not be randomly selected: empirically improving recruitment success and diversity in developmental psychology research
Bibliographic record
Abstract
Psychological and developmental research have been critiqued for the lack of diversity of research samples. Because differences in culture, race, and ethnicity can influence participant behavior, limited diversity limits the generalizability of the findings. These differences may also impact how participants behave in response to recruitment attempts, which suggests that recruitment itself may be leveraged to increase sample diversity. The goal of the current study was to determine what factors, within a recruitment interaction, could be leveraged to increase success and diversity when recruiting families with children for developmental research. Study 1 found three factors influenced success: (1) recruitment was more successful when other potential participants were also interested (i.e., recruiters were busy), (2) recruiters of particular races were more successful than recruiters of other races, and (3) differences in success were related to what the recruiter said to engage the potential participant (i.e., the script). The latter two factors interacted, suggesting some recruiters were using less optimal scripts. To improve success rates, study 2 randomly assigned scripts to recruiters and encouraged them to recruit more vigorously during busy periods. Study 2 found that two factors influenced success: (1) some scripts were more successful than others and (2) we were more successful at recruiting non-White potential participants than White participants. These two interacted, with some scripts being more successful with White and other scripts being more successful with non-White families. This intervention significantly increased recruitment success rate by 8.1% and the overall number of families recruited by 15.3%. These findings reveal that empirically evaluating and tailoring recruitment efforts based on the most successful strategies is effective in boosting diversity through increased participation of children from non-White families.
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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 | 0.338 | 0.573 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".