Meeting Needs and Seeking Peace: Experiences of Micro-Finance Loan Recipient Women of Karachi, Pakistan
Bibliographic record
Abstract
The association between socio-economic status and health is well established. While involvement in a micro-finance program has been shown to reduce poverty among women, little is known about how this involvement impacts their mental health. Using interpretive descriptive methodology, this qualitative study explored women’s perceptions of how their participation in micro-finance programs influenced their mental health. Data were collected and analyzed through interviews with 32 urban-dwelling women from Karachi, Pakistan who have been micro-finance loan recipients for a period of 1 to 5 years. Women recognized micro-finance programs as being a major inspiration towards enhancing their mental health. The majority of participants, regardless of the number of years they held a micro-finance loan, revealed that seeking micro-loans and establishing income-generation activities assisted them to reduce tensions related to meeting their fundamental needs. Among the few participants who were not experiencing positive mental health at the time of the interview, they could foresee hope towards a better and an improved state of mental health. The need for and the importance of vocational skills training, economic stability, opportunity for education and environmental safety were echoed by these “everyday women” of Pakistan. Multiple stakeholders and micro-finance program should work collaboratively for the promotion of mental health determinants.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".