The Privatization Imperative: Women Negotiating Healthcare in Kandy, Sri Lanka
Bibliographic record
Abstract
Since the 1980s, poorer countries have shifted health reform efforts from strengthening public systems to increasing the private sector’s role in healthcare provision. Empirical research on healthcare access focuses on quantifying out-of-pocket payments or service utilization, making invisible both user experiences and how the dynamics of public and private provision are contingent on place and social relations. Historically a model for poorer countries, Sri Lanka’s public healthcare system is seeing steady privatization following decades of insufficient state investment alongside incentivized private expansion. However, little is known about what this restructuring means for healthcare access in Sri Lanka. I employed a Third World Marxist feminist qualitative methodology to explore how the presence of private healthcare shapes access for women in Kandy, Sri Lanka. I asked: Where do women go for healthcare? What are their impressions of the health services they use? How do they navigate public and private systems? And how are these questions shaped by social location? Using focus groups, interviews, meetings, and a short survey, I gathered data from 40 residents of Udawatta Division. My analysis linked macrostructures and processes of healthcare restructuring with women’s everyday experiences of utilizing healthcare. Economic exclusion and quality concerns limited the range of private healthcare ‘choices’ available to users. Almost all women mixed public and private services, with these hybrid arrangements differing by social location. Economically disadvantaged users were compelled to consume private healthcare owing to service deficits in the public system. Middle-class women mostly used private outpatient services, and exploited ‘dual practice’ to access more responsive public inpatient care. Socially disadvantaged women, particularly ethnic minorities, relied on the same pathway to avoid neglect and/or abuse within the public system. The state’s ‘withdrawal’ from healthcare provision, and its incentives for private expansion, has wide-ranging implications for users in Kandy. As women struggle to address service gaps in the public sector, hybrid arrangements stratify services along class and ethnic lines, creating opportunities for private accumulation. My findings interrogate the direction of Sri Lanka’s health reform and call into question global health advocacy for ‘mixed health systems’ as a path to achieving ‘Universal Health Coverage.’
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".