Women’s Contributions to Biomedical Healthcare in Ghana: A Focus on Obuasi
Bibliographic record
Abstract
From economic, through politics to domestic support, women have been the major engineers of valuable roles towards the development of every culture. Historically, their impacts in medicine and healthcare in general have been evident across time and space. Prior to European influx and the modernization of healthcare in Ghana, women delivered such roles that simulate that of modern midwives, nurses, herbalists and priestesses. Although, denied access to formal education in the colonial days, because of cultural reasons, women have risen to occupy central stages in biomedical services. Regardless of their numerical strength and contributions towards the provision of healthcare, they have been neglected and marginalized both within the society and by scholars. Significantly, the place of Obuasi, in particular, within the literature on women’s contribution to healthcare delivery has received little attention. Dwelling on a qualitative research approach grounded in both primary and secondary data, the current study attempted a prime discourse on the contribution of women in the biomedical spheres using the Obuasi community as a case study. The current study has revealed that women as nurses and midwives work toward reducing child mortality and improvement of maternal health. Also, we have analyzed the challenges women face within the biomedical sphere as nurses and midwives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".