Evaluating pregnant women's action plans for exercise: Content, compliance rates, and resultant exercise patterns
Bibliographic record
Abstract
Despite the benefits associated with exercise during pregnancy, many pregnant women are inactive, highlighting the need for effective interventions for this population. This study explores the content of pregnant women's action plans for exercise, their resultant exercise patterns, and the correspondence between their action plans and objectively-measured follow-up behaviour. Participants (M age = 30.68, SD = 4.51; M weeks pregnant = 21.81, SD = 5.60) were 31 previously inactive pregnant women who formulated action plans (i.e., what, when, where and with whom they intended to exercise) as part of a larger intervention based on the Health Action Process Approach (Schwarzer, 2003). Week-long follow-up exercise was assessed via accelerometer, and data were used to determine the number of bouts of moderate-to-vigorous exercise lasting 30 or more minutes and when (day and time) each bout occurred. Participants engaged in an average of 4.39 bouts (SD = 1.33) with 35% of bouts occurring after 6pm. Fifty-eight percent of action plans directly translated into observed bouts (same day and time) while an additional 26% of plans led to exercise on the same day but at a different time. Furthermore, participants who successfully achieved 3 or more plans engaged in more bouts than those who achieved fewer than 3 plans (p < .05, ?2 = .12). The usefulness of action planning as a strategy for increasing exercise among pregnant women will be discussed.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".