Hormonal Contraceptive Use Among Canadian Pre-menopausal Women
Bibliographic record
Abstract
Sufficient research involving female athletes and how their hormonal milieu can influence cardiovascular function and performance is lacking. Prior work has predominately focused on the effects of oral contraceptives, yet there is a growing trend of women using long-acting reversible contraceptive (LARC) methods. As a first step to laying the foundation of novel studies assessing hormonal influences on cardiovascular indices, an updated survey of hormonal contraception use is needed. PURPOSE: To determine the current landscape of hormone contraceptive usage among Canadian women and how this use may be influenced by exercise status. METHODS: An online survey was distributed to pre-menopausal females aged 19-49 via a third-party survey company. The survey included questions on respondents’ demographics, current and past hormonal contraceptive use, and physical activity levels. Prevalence of current hormonal contraceptive use was calculated by age. Chi-squared tests were conducted to determine whether there was an association between contraceptive choice and self-identified level of physical activity. RESULTS: 2678 eligible respondents completed the survey, but after removals due to illogical or missing data, responses of 2306 female Canadians (age 33.4 ± 8.1 years) were analyzed. Twenty-nine percent of these respondents were currently using hormonal contraceptives. The most common choices were some form of oral contraceptives (56.4%), intrauterine devices (IUD) (28.4%) and vaginal rings (5.1%). Over 30% of hormonal contraceptive users were currently using a LARC method. There was a significant relationship between current hormonal contraceptive type and whether the respondents were physically active (p < 0.001) with more female exercisers choosing IUDs (30.3%) than those who are non-exercisers (25.3%). CONCLUSION: These findings demonstrate a change in hormonal contraception use, notably an increase in use of IUDs among Canadian women over the last 15 years. This study also shows a high prevalence of alternative contraceptive options that may influence hormone levels differently than oral forms. The relationship between current exercise status and contraceptive choice illustrated in this study, merits consideration of these differences in future study design.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".