Development of sport policy and practice recommendations for pregnant, postpartum and parenting Canadian high-performance athletes
Bibliographic record
Abstract
Our objective is to describe the development of evidence-based policy and practice recommendations for pregnant, postpartum and parenting Canadian high-performance athletes. A community-based participatory research approach was employed as the study design, and data were generated via a rapid review of existing sport policy for pregnant and postpartum athletes, followed by an extensive consultation and engagement process with key sport stakeholders via survey and one-on-one and group interviews. 102 sport stakeholders participated via the survey (n=56), individual and group interviews (n=33), and follow-up interviews (n=13). Individuals represented a range of summer/winter Olympic and Paralympic athletes, medical and support staff, National Sport Organisations and Sport Canada representatives. Seven evidence-based policy and practice recommendations were developed for Sport Canada decision-makers. Recommendations include the need for Sport Canada to (a) establish two new cards for pregnant and parenting athletes, (b) develop a policy to support pregnant, postpartum and parenting athletes, (c) create new funding sources for facilities that accommodate the needs of pregnant, postpartum and parenting athletes, (d) create new funding source for athletes to train and/or compete during infants' first year, (e) develop training and educational modules related to pregnant, postpartum and parenting athletes, (f) increase visibility of pregnant, postpartum and parenting athletes and (g) invest in research on high-performance sport participation during and following pregnancy. The collaborative processes employed in this research serve as a model for sports organisations to develop evidence-based policies and practices that can support pregnant, postpartum and parenting athletes.
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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 | 0.065 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".