'We Women Worry a Lot About Our Husbands': Ghanaian women talking about their health and their relationships with men
Bibliographic record
Abstract
Discussions of the health of women in the developing world have typically been shaped by the concerns of policy makers, health care professionals and other experts. They have focused on reproductive health and, above all, women have been defined in terms of their childbearing role. Yet when women themselves are given a voice, a different set of issues emerges. The research reported here aimed to explore women's own concerns about their health and how they understand their health problems. The study was conducted in the Volta region of Ghana and it included interviews with 75 women of varying background. Almost three-quarters of the women reported 'thinking too much' and many also said that they had problems sleeping, suffered frequent headaches and often felt unhappy or sad. They explain these psycho-social health problems in terms of their social and material circumstances and one of the main themes women emphasised was their relationships with men. Relying on women's accounts, we trace the ways in which they conceptualised their health, seeing it as shaped by their lack of control over the conditions of their lives; gender relations define their responsibilities while at the same time withholding the control and resources they require in order to achieve a measure of economic independence and predictability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".