Effect of Perceived Weight Gain on Hormonal Contraception Choice for Women: A Review
Bibliographic record
Abstract
Abstract Background: Hormonal contraception plays a pivotal role in protecting against unintended pregnancies and has been developed to provide options that best fit women's lifestyles. However, negative perceptions can alter women's attitudes, which can prohibit the usage of hormonal contraception. This review aimed to collect information surrounding perceptions of hormonal contraception side effects, specifically weight gain and women's contraceptive choice. Methods: 703 articles were found through searching three electronic databases; EBSCO, PubMed, and Web of Science, in addition to Google Scholar. Articles were included if they were published between 2009-2020, could be translated to English, included any form of hormonal contraception, and reported perceived weight gain. A total of 39 articles met the inclusion criteria and are included in the review. Results: Within those articles, there were six overarching themes: (1) negative perception of weight gain, (2) fear of weight gain, (3) contraception decision based on obesity concerns, (4) avoidance and discontinuation of method due to concerns of weight gain, (5) limited contraceptive knowledge, and (6) lack of counseling. It was found that negative perceptions of weight gain influence women’s hormonal contraception perception and attitude. Conclusion: Negative perceptions are derived from experience, misconception, and lack of knowledge, leading to fear, avoidance, or discontinuation. Understanding women's perceived weight gain and perception towards contraceptives can help assess its effect on women's choice of contraception. This information can aid health care professionals in educating and discussing methods that would best fit women and improve hormonal contraception adherence.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".