Decentralising the health sector in Zambia: An observational study
Bibliographic record
Abstract
Background: Zambia has embarked on the devolution of various health functions to the subnational levels at provincial and district level. Objectives: (i) To examine the decentralisation process in Zambia using the lens of the Constitution, enabling pieces of legislation, the relevant policy frameworks and (ii) To examine the decentralisation implementation process using the change management process. Methodology: This was an observational study involving both qualitative and quantitative methods using purposive selection of participants from the District Health Offices, Health facilities and the Local authorities. Interviews and focus group discussions were used to collect the data over a period covering January and February 2024 in three sites namely Mazabuka, Lusaka and Chongwe Districts of Zambia. Key Findings: Inadequate legal and policy framework, operationalization of the decentralisation policy, the acceleration of the implementation before the legal framework, as well as other change management processes and procedures were identified as key challenges. Conclusion: While there are adequate constitutional provisions, inadequacies in policy and legal frameworks, implementation capacity in councils, community participation, change management process as well as the collaborative framework to facilitate smooth and effective implementation of decentralizing some health functions to the Local Authorities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".