Healthcare Needs of Migrant Female Head Porters in Ghana: evidence from the Greater Accra and Greater Kumasi Metropolitan areas
Bibliographic record
Abstract
Abstract Background In low-and middle-income countries, migrants are confronted with several healthcare needs which affects the promotion of well-being and healthy lives. However, not much is known about the healthcare needs of migrant female head porters (Kayayei) in Ghana. This study assesses the healthcare needs of migrant female head porters in Greater Kumasi Metropolitan Area (GKMA) and Greater Accra Metropolitan Area (GAMA). Methods The study adopted a convergent mixed methods design where both qualitative and quantitative data were used. A random sample size of 470 migrant female head porters was used for the study. Results The study revealed that ante-natal care, post-natal care, treatment of malaria, treatment of diarrhoea diseases, mental health, sexual health, and cervical screening were healthcare needs of migrant female head porters. The findings show that participants from the GAMA significantly have greater cervical screening needs (71.6% vrs 67.1%, p = 0.001) compared to those from the GKMA. Kayeyei from the GKMA significantly have greater mental health needs than those from the GAMA (84.6% vrs 79.2%, p = 0.031). Also, Kayeyei from the GKMA significantly attend post-natal care compared to those from the GAMA (99.4% vrs 96.2%, p = 0.013). Conclusion The findings underscore differential healthcare needs across geographical localities. Based on the findings of the study, specific healthcare needs such as ante-natal care and post-natal care should be included in any health programmes and policies that aim at addressing healthcare needs of migrant female head porters in the two metropolitan areas of Ghana.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".