‘Every medicine is part poison’: a qualitative inquiry into the perceptions and experiences of choosing contraceptive methods of migrant Chinese women living in Australia
Bibliographic record
Abstract
Abstract Background In Australia, ethnic Chinese people are one of the largest, youngest and fastest growing overseas-born groups. Yet, little is known about their perceptions of contraceptive methods and their experiences with choosing one. Decisions about contraceptive methods are preference sensitive. Understanding the influencing factors of Chinese migrant women’s contraceptive method choice and practices will help cater to their decision-making needs in a culturally sensitive and responsive way. Methods A qualitative study design underpinned by critical realism approach was used to explore Chinese migrant women’s perceptions and experiences of choosing contraceptive methods. Semi-structured interviews were conducted with 22 women who self-identified as being ethnically Chinese and had been living in Australia for no more than 10 years. The interview guide was adapted from the Ottawa Decision Support Framework. Majority of the interviews were conducted in Mandarin Chinese. Transcribed data was analysed using thematic analysis method. Results Four major themes were identified, including: ‘every medicine is part poison: hormonal contraceptives cause harm to the body’; ‘intrauterine device, a device used in the past for married women’; ‘it takes two (or one) to decide, depending on the relationship dynamics and contraception preferences’; and ‘it is not necessary to seek medical advice in choosing contraceptive methods’. Conclusions Our findings suggest that Chinese migrant women’s perceptions and experiences of choosing contraceptive methods are influenced by complex personal, cultural, societal and inter-relational factors. Chinese migrant women were cautious of using hormonal methods due to fears of side-effects, including reduced or absent menstrual bleeding. Women were also reluctant to consider intrauterine devices as options due to associating them with past experiences of other women and themselves and also fears of potential complications. There was a reluctant attitude towards seeking medical advice regarding contraception due to beliefs that needing to use contraception is not an illness requiring treatment. Such findings are likely to be useful in increasing healthcare professionals’ and policy makers’ understanding of Chinese migrant women’s contraceptive method preferences, beliefs and behaviours. They also help to develop culturally and linguistically sensitive strategies, which goes beyond the provision of contraceptive counselling, in assisting Chinese migrant women’s decision-making needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.290 | 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 teacher head, 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".