Challenges of implementing sustainable health care delivery in Nigeria under environmental uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: The increasing cost of health care in developed and developing economies has called for a change in the way health activities are implemented. Nigeria is faced with fundamental health care related challenges coupled with recent security issues. Uncertainty prevails as health system dynamics unfolds. Objectives: To explore the relationship between environmental uncertainty and health care delivery system in Nigeria. The study aims at reviewing the dynamics of health care delivery in some developed economies and Nigeria with regard to methods of adaptation of health care under uncertainty, and developing a framework for sustainable health care delivery. Methods: Databases were searched for relevant literatures using the following keywords: environmental and health uncertainty, Nigerian health care system, Nigerian primary health care, health care financing and sustainability. Other keywords used include: US, Europe and China health care dynamics, among others. Scientific databases obtained from the Internet were used including online journals, which were sourced mainly from the Google. Relationships if any were established and a framework for sustainability developed. Results: Environmental uncertainty has a multiplicity of interactions with different aspects of health care system, resulting in poor infrastructural development, inadequate government funding, absence of integrated system for disease prevention and surveillance, policy reversals, security challenges, and unimpressive health indicators in Nigeria. A framework for implementing sustainable health care delivery under uncertainty is proposed. Discussion and conclusion: Uncertainty abounds in the Nigerian health care delivery system; causing further distortion in development of the health sector. Effective mobilization of health care professionals, use of sustainable care plans by government, use of integrated medical intelligence and surveillance systems, accountability, commitment, and above all quality leadership - will minimize uncertainty factors and enhance health care performance and sustainability in Nigeria.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it