Development of Non-Profit Organisations Providing Health and Social Services in Rural South Africa: A Three-Year Longitudinal Study
Bibliographic record
Abstract
INTRODUCTION: In an effort to increase understanding of formation of the community and home-based care economy in South Africa, we investigated the origin and development of non-profit organisations (NPOs) providing home- and community-based care for health and social services in a remote rural area of South Africa. METHODS: Over a three-year period (2010-12), we identified and tracked all NPOs providing health care and social services in Bushbuckridge sub-district through the use of local government records, snowballing techniques, and attendance at NPO networking meetings--recording both existing and new NPOs. NPO founders and managers were interviewed in face-to-face in-depth interviews, and their organisational records were reviewed. RESULTS: Forty-seven NPOs were formed prior to the study period, and 14 during the study period--six in 2010, six in 2011 and two in 2012, while four ceased operation, representing a 22% growth in the number of NPOs during the study period. Histories of NPOs showed a steady rise in the NPO formation over a 20-year period, from one (1991-1995) to 12 (1996-2000), 16 (2001-2005) and 24 (2006-2010) new organisations formed in each period. Furthermore, the histories of formation revealed three predominant milestones--loose association, formal formation and finally registration. Just over one quarter (28%) of NPOs emerged from a long-standing community based programme of 'care groups' of women. Founders of NPOs were mostly women (62%), with either a religious motivation or a nursing background, but occasionally had an entrepreneurial profile. CONCLUSION: We observed rapid growth of the NPO sector providing community based health and social services. Women dominated the rural NPO sector, which is being seen as creating occupation and employment opportunities. The implications of this growth in the NPO sector providing community based health and social services needs to be further explored and suggests the need for greater coordination and possibly regulation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".