Strengths, disconnects and lessons in local and central governance of the response to the first wave of COVID-19 in Ghana
Bibliographic record
Abstract
Objectives: To explore governance, coordination and implementation actors, structures and processes, facilitators, and barriers within local government and between central and local government in Ghana's COVID-19 response during the first wave of the outbreak. Design: Cross-sectional single case study. Data collection involved a desk review of media, policy and administrative documents and key informant in-depth interviews. Setting: Two municipalities in the Greater Accra region of Ghana. Participants: Local government decentralised decision makers and officials of decentralised departments. Interventions: None. Main Outcome Measures: None. Results: Coordination between the national and local government involved the provision of directives, guidelines, training, and resources. Most of the emergency response structures at the municipal level were functional except for some Public Health Emergency Management Committees. Inadequate resources challenged all aspects of the response. Coordination between local government and district health directorates in risk communication was poor. During the distribution of relief items, a biased selection process and a lack of a bottom-up approach in planning and implementation were common and undermined the ability to target the most vulnerable beneficiaries. Conclusions: Adequate financing and equipping of frontline health facilities and workers for surveillance, laboratory and case management activities, transparent criteria to ensure effective targeting and monitoring of the distribution of relief items, and a stronger bottom-up approach to the planning and implementation of interventions need to be given high priority in any response to health security threats such as COVID-19. Funding: This work is funded by International Development Research Centre Grant No. 109479, Exploring and learning from evidence, policy and systems responses to COVID-19 in West and Central Africa.
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How this classification was reachedexpand
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 | 0.003 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".