Community attitudes and gendered influences on decision making around contraceptive implant use in rural Papua New Guinea
Bibliographic record
Abstract
BACKGROUND: Despite targeted interventions to improve contraceptive implant acceptability and uptake in rural Papua New Guinea (PNG), ongoing use of this method remains limited. Previous literature has suggested community attitudes and intrinsic factors within the decision-making process may be negatively impacting on implant uptake, however these elements have not previously been studied in detail in this context. We set out to explore community attitudes towards the contraceptive implant and the pathways to decision making around implant use in a rural community on Karkar Island, PNG. METHODS: We conducted 10 focus-group (FGD) and 23 in-depth interviews (IDI) using semi-structured topic guides. Key sampling characteristics included age, exposure or non-exposure to implants, marital status, education and willingness to participate in discussion. Four FGDs were held with women, four with men and two with mixed gender. IDIs were carried out with five women (current implant users, former implant users, implant never users), five men, five religious leaders (Catholic and non-Catholic), four village leaders and four health workers. Two in-depth interviews (four participants) were analysed as dyads and the remaining participant responses were analysed individually. RESULTS: Men were supportive of their wives using family planning but there was a community-wide lack of familiarity about the contraceptive implant which influenced its low uptake. Men perceived family planning to be 'women's business' but remained strongly influential in the decision making processes around method use. Young men were more receptive to biomedical information than older men and had a greater tendency towards wanting to use implants. Older men preferred to be guided by prominent community members for decisions concerning implants whilst young men were more likely to engage with health services directly. CONCLUSIONS: In communities where a couple's decision to use the contraceptive implant is strongly coloured by gendered roles and social perceptions, having a detailed understanding of the relational dynamics affecting the decision-making unit is useful in targeting future healthcare interventions. Engaging groups who are reluctant to connect with health information, as well as those who are most influential in the decision making process, will have the greatest impact on increasing implant acceptability and uptake.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".