The state of decentralization of the healthcare system and nutrition programs in the Lao People’s Democratic Republic: an organizational study
Bibliographic record
Abstract
BACKGROUND: The Lao People's Democratic Republic (Lao PDR), a lower-middle income country, has a higher malnutrition rate than other Southeast Asian countries. The decentralization of healthcare is a determinant of the effectiveness of programs to reduce malnutrition, but no study has focused on this factor in this country. This organizational study explores the state of decentralization of the healthcare system in Lao PDR that underlies the nutrition programs in the country. METHODS: A qualitative study, which is based on a neo-institutional theory conceptual framework, explored factors related to dominant structure (laws, regulations, resources) and interpretative schemes (dominant ideas and beliefs) that characterize the nutrition services provided in the Lao healthcare system. Twenty-four semistructured interviews were performed with representatives of healthcare institutions involved in nutrition programs at different government levels, external donors and civil society organizations. The interviews were completed with relevant documents. The analysis focused on the convergence of interpretative schemes of the organizations concerned and the coherence between the structure underpinning the nutrition programs and the interpretative schemes. RESULTS: Services deployed to reduce malnutrition in the Lao PDR remain largely centralized, despite factors specific to the country that led it to promote decentralization of its services. The convergence of interpretive schemes and the coherence between the observed structure and the interpretative schemes of actors at all governance levels ensure the stability of this state of decentralization, which has persisted for almost 50 years. CONCLUSION: Nutrition programs in the Laos PDR are largely under the responsibility of the central government. The transformations in the healthcare system, notably with the use of new information technologies and the fact that the provinces are populated by a growing number of professionals trained in nutrition in addition to factors that push the system to be decentralized, such as ethnic diversity, the increasing availability of human resources in provinces, and the use of communication technologies, are not strong enough to change the balance of power between governance levels. The deconcentration that characterizes decentralization is therefore likely to continue for the foreseeable future.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".