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Record W4283213740 · doi:10.3989/chdj.2022.009

Women’s Contributions to Biomedical Healthcare in Ghana: A Focus on Obuasi

2022· article· en· W4283213740 on OpenAlexaff
Samuel Adu‐Gyamfi, Benjamin Dompreh Darkwa, Regina Adwoa Agyeiwaa Boateng, Lucky Tomdi

Bibliographic record

VenueCulture & History Digital Journal · 2022
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of New BrunswickUniversity of Alberta
Fundersnot available
KeywordsHealth careModernization theoryPoliticsWork (physics)Face (sociological concept)Qualitative researchFocus groupHealthcare systemSpace (punctuation)Context (archaeology)SociologyPolitical scienceGender studiesPublic relationsMedicineNursingSocial scienceGeographyLawAnthropologyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.260
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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