The Medicine That Might Kill the Patient: Structural Adjustment and Its Impacts on Health Care in Bangladesh
Bibliographic record
Abstract
Over the past decade, reforms of the health sector have evolved as a global phenomenon. There is, by now, a fair literature on the relationship between globalization and health. Within this literature, however, there is relatively little attention given to the Structural Adjustment Program (SAP), one aspect of globalization, and its impact on health. It can be observed that the SAP has had a dramatic impact on the status of education, health, the environment, and women and children in many developing countries. The restructuring of the health sector has led to the collapse of preventive and curative care due to the lack of medical equipment, supplies, poor working conditions, low pay of medical personnel, and the resulting low morale in Ghana, Philippines, and Zimbabwe. User fees in primary health care have led to the exclusion of a large section of the population from accessing health services as they are unable to pay. This article discusses the health specific impact of the SAP and the economic reforms initiated under it in Bangladesh. In particular, it will analyze how these policies affect the health care delivery system in Bangladesh in relation to geographic accessibility, affordability, quality of services, administrative efficiency, the rural urban service gap, public provision of health care, and donor influence on health policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".