Social franchising in healthcare: a systematic review and narrative synthesis of implementation and outcomes
Bibliographic record
Abstract
INTRODUCTION: The growing prominence of social franchising in healthcare underscores the need to analyse its implementation and impacts; however, substantial research gaps remain. Therefore, we aimed to conduct a systematic review and narrative synthesis of evidence to analyse the social franchise models, implementation and outcomes. METHODS: We conducted a systematic literature search in February 2024 on Medline, Embase, PubMed, Web of Science, CINAHL and Scopus using terms related to 'social franchising' in healthcare. We conducted a qualitative narrative synthesis of study findings into five thematic areas: client impact and utilisation, healthcare outcomes, financial sustainability, innovative technologies and awareness activities. RESULTS: From 4184 search results, 47 studies were included in the analysis. We identified 29 social franchises across 25 countries. Social franchises were mostly present in Africa, Asia and Central America. Most franchises focused on sexual, reproductive and maternal health (n=18) and family planning (FP) (n=25), and most included training (n=21), service provision (n=17) and financial support (n=12). Franchising improved client volumes, satisfaction and contraceptive continuation rates and increased access to healthcare. Vouchers and subsidised services reduced the financial burden among clients. Telemedicine and call centres enhanced healthcare delivery, and community outreach and marketing increased awareness and modern contraceptive use. However, franchises struggled to reach poorer populations due to high fees and competition from public services. It often did not improve FP, reproductive healthcare and child nutrition and had limited branding and promotional activities. Additionally, heavy reliance on donor funding threatened long-term sustainability. CONCLUSION: Social franchising presents a potential strategy for expanding healthcare access and improving service delivery, though outcomes regarding the effectiveness of social franchising vary across regions. More research is needed to evaluate digital technology use and the long-term impact, equity and sustainability of social franchising. PROSPERO REGISTRATION NUMBER: CRD42022328104.
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
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 | 0.056 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".