Exploring the postpartum return to sport and performance in Canadian elite athletes
Bibliographic record
Abstract
Background: Athlete-mothers in elite sport were viewed as anomalies until very recently. Perhaps as a consequence of limited research, support and resources available for pregnant and postpartum athletes may be inadequate. Objective: To explore the experiences of athletes returning to sport and performance postpartum. Methods: Ten elite Canadian athletes who became pregnant during their sporting career and attempted to return to competitive sport after childbirth completed an online questionnaire. Results: Most participants reported that timing of conception was planned around competition schedule. During pregnancy, most athletes trained through pregnancy, but found the advice they received unsatisfactory. Lack of support for childcare within sport and balancing breastfeeding with training were commonly identified challenges. Nine of the 10 athletes reported resuming training after childbirth and five returned to competitive sport, of which four reported improved performance. Conclusion: Elite athletes in our sample were able to return to a high level of sport after childbirth, however many expressed the need for improved support through pregnancy and the postpartum period. This exploratory study reveals promising findings of how experiences are improving for pregnant and postpartum elite athletes. This sets the stage for more research to promote sport participation by athlete mothers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".