The most effective strategy for recruiting a pregnancy cohort: a tale of two cities
Bibliographic record
Abstract
BACKGROUND: Pregnant women were recruited into the Alberta Pregnancy Outcomes and Nutrition (APrON) study in two cities in Alberta, Calgary and Edmonton. In Calgary, a larger proportion of women obtain obstetrical care from family physicians than from obstetricians; otherwise the cities have similar characteristics. Despite similarities of the cities, the recruitment success was very different. The purpose of this paper is to describe recruitment strategies, determine which were most successful and discuss reasons for the different success rates between the two cities. METHODS: Recruitment methods in both cities involved approaching pregnant women (< 27 weeks gestation) through the waiting rooms of physician offices, distributing posters and pamphlets, word of mouth, media, and the Internet. RESULTS: Between May 2009 and November 2010, 1,200 participants were recruited, 86% (1,028/1,200) from Calgary and 14% (172/1,200) from Edmonton, two cities with similar demographics. The most effective strategy overall involved face-to-face recruitment through clinics in physician and ultrasound offices with access to a large volume of women in early pregnancy. This method was most economical when clinic staff received an honorarium to discuss the study with patients and forward contact information to the research team. CONCLUSION: Recruiting a pregnancy cohort face-to-face through physician offices was the most effective method in both cities and a new critically important finding is that employing this method is only feasible in large volume maternity clinics. The proportion of family physicians providing antenatal and post-natal care may impact recruitment success and should be studied further.
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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 | 0.096 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".