Innovative health service delivery models in low and middle income countries - what can we learn from the private sector?
Bibliographic record
Abstract
BACKGROUND: The poor in low and middle income countries have limited access to health services due to limited purchasing power, residence in underserved areas, and inadequate health literacy. This produces significant gaps in health care delivery among a population that has a disproportionately large burden of disease. They frequently use the private health sector, due to perceived or actual gaps in public services. A subset of private health organizations, some called social enterprises, have developed novel approaches to increase the availability, affordability and quality of health care services to the poor through innovative health service delivery models. This study aims to characterize these models and identify areas of innovation that have led to effective provision of care for the poor. METHODS: An environmental scan of peer-reviewed and grey literature was conducted to select exemplars of innovation. A case series of organizations was then purposively sampled to maximize variation. These cases were examined using content analysis and constant comparison to characterize their strategies, focusing on business processes. RESULTS: After an initial sample of 46 studies, 10 case studies of exemplars were developed spanning different geography, disease areas and health service delivery models. These ten organizations had innovations in their marketing, financing, and operating strategies. These included approaches such a social marketing, cross-subsidy, high-volume, low cost models, and process reengineering. They tended to have a narrow clinical focus, which facilitates standardizing processes of care, and experimentation with novel delivery models. Despite being well-known, information on the social impact of these organizations was variable, with more data on availability and affordability and less on quality of care. CONCLUSIONS: These private sector organizations demonstrate a range of innovations in health service delivery that have the potential to better serve the poor's health needs and be replicated. There is a growing interest in investing in social enterprises, like the ones profiled here. However, more rigorous evaluations are needed to investigate the impact and quality of the health services provided and determine the effectiveness of particular strategies.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".