Enrollment, Childbearing Motivations, and Intentions of Couples in the Creighton Model Effectiveness, Intentions, and Behaviors Assessment (CEIBA) Study
Bibliographic record
Abstract
CONTEXT: System (CrM) is a standardized approach for educating women about the biomarkers of their fertility. Couples can use this information for timing intercourse during "fertile" or "infertile" days in order to try to conceive or to avoid pregnancy. OBJECTIVES: The study of Creighton Model Effectiveness, Intentions, and Behaviors Assessment (CEIBA) was conducted to assess fertility motivations, intentions, fertility-related sexual behaviors, and their impact on effectiveness to avoid and to conceive among new users of the CrM. This paper reports enrollment baseline characteristics. SETTINGS AND DESIGN: Centers; 16 in the USA and one in Toronto, Canada. MATERIALS AND METHODS: Couples who were new or returning users of the CrM were eligible. Couples who were initially trying to conceive or had a history of subfertility were excluded. Couples were enrolled and followed prospectively by their CrM instructors and also by CEIBA study staff. They completed baseline questionnaires. RESULTS: 1,132 new couples were assessed; 1,090 (96%) couples were screened; 429 (39%) couples were eligible; 305 women (71%) and 290 (95%) male partners were enrolled. The majority of women was engaged (39%) or married (51%), college graduates (77%), Caucasian non-Hispanic (80%), and Roman Catholic (80%). The most common reasons for learning CrM (women) were to use a natural method for family planning (91%), for moral/ethical/religious reasons (70%), the lack of side effects (71%), or insight into the menstrual cycle and fertility (62%). Women and men intended to have a mean of three and two additional children, respectively. Of women, 21% intended to have a child within a year and 60% between 1 and 3 years. The mean positive childbearing motivation score was 3.3 for both women and men (range 1-4, with 4 being most positive). CONCLUSION: Couples beginning use of the CrM to avoid pregnancy have high levels of motivation, desire, and intention for future childbearing. The CEIBA study has prospective measures of desires, intentions, and sexual/fertility behaviors for up to 1 year. We will assess the impact of desires, intentions, and behaviors on the pregnancy rates among these couples.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".