Self-Directed Female Migration in Ghana: Health and Wellness of Elderly Family Caregivers Left Behind. An Ethnographic Study
Bibliographic record
Abstract
Driven by the global economic crisis, families are developing strategies for survival, including self-directed female migration. Female migration has negative and positive impacts on families in rural areas. The purpose of the project was to explore the health and wellness experiences of elderly family caregivers who have female family members who have migrated to improve the status of their families. In this focused ethnographic study, we interviewed elderly family members who had a female family member who migrated outside their community for employment. Participants were enrolled from northern Ghanaian communities known to be economically disadvantaged in comparison to their southern counterparts. All interviews were audio-recorded, transcribed verbatim, and translated into English. Data were analyzed based on thematic content. Majors themes that emerged were reasons for children leaving their families; physical, emotional, and spiritual health; and social and economic struggles. Challenges of family care work undertaken by the elderly in families with emigrated female kin strongly also emerged as a theme. New contextual knowledge was developed about the impact of self-directed female migration on the health and wellness of elderly family caregivers. The information is valuable for the development of culturally appropriate social support and health practices for female migrants and their families.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".