Exploring the lived experiences of women in larger bodies who access and use contraception in Ontario
Bibliographic record
Abstract
Background: Contraception is widely used by women to prevent pregnancy and for other health-related reasons. In Canada, many contraceptive users may be living in larger bodies. While there is a growing interest in the reproductive health care experiences of women in larger bodies, less attention has been paid to contraception. Research suggests that women in larger bodies may receive inadequate contraceptive counselling and care; however, women’s voices are rarely captured in this research. \nObjectives: This study aimed to understand the lived experiences of women in larger bodies who access and use female-oriented contraception in Ontario. Specifically, this study examined: (1) How is contraceptive use embodied by women in larger bodies; (2) How do women in larger bodies navigate contraceptive care; and (3) How can women in larger bodies’ experiences with contraception be improved? \nMethods: This phenomenological study explored the lived experiences of 19 women in larger bodies who accessed and used female-oriented methods of contraception in Ontario within the last five years. In-depth interviews were conducted with participants and the data were analyzed by applying a feminist lens to interpretive phenomenological analysis. \nResults: There was significant diversity in women in larger bodies’ lived experiences with contraception. The notion of control was central to participants’ embodied experiences with routine contraceptive use, whereas experiences with contraceptive care were shaped by feelings of (dis)empowerment. While many participants felt supported in some capacity, women in larger bodies also suggested that their contraceptive counselling and care needs were not always met. Finally, three major stages of participants’ experiences with emergency contraception were detailed, revealing the significance of personal context in shaping their surrounding experiences. \nConclusion: This study highlights the complexity and diversity of women in larger bodies’ lived experiences with routine and emergency contraception. The findings suggest that non-judgemental, patient-centred approaches to contraceptive counselling and care are needed to better support women in larger bodies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".