The “No bed syndrome” in Ghana — what, how and why? A literature, electronic and print media review
Bibliographic record
Abstract
Objectives: "No bed syndrome" has become a familiar phrase in Ghana. Yet, there is very little in medical texts or the peer reviewed literature about it. This review aimed to document what the phrase means in the Ghanaian context, how and why it occurs, and potential solutions. Design: A qualitative desk review using a thematic synthesis of grey and published literature, print and electronic media content covering the period January 2014 to February 2021. Text was coded line by line to identify themes and sub-themes related to the research questions. Analysis was manual and with Microsoft Excel to sort themes. Setting: Ghana. Participants and Intervention: Not applicable. Results: "No bed syndrome" describes the turning away by hospitals and clinics of people seeking walk in or referral emergency care with the stated reasons "no bed available" or "all beds are full". There are reported cases of people dying while going round multiple hospitals seeking help and being repeatedly turned away because there is "no bed". The situation appears to be most acute in the highly urbanized and densely populated Greater Accra region. It is driven by a complex of factors related to context, health system functions, values, and priorities. The solutions that have been tried are fragmented rather than well-coordinated whole system reform. Discussions and recommendations: The "no bed syndrome" describes the challenge of a poorly functioning emergency health care system rather than just the absence of a bed on which to place an emergency case. Many low and middle income countries have similar challenges with their emergency health care systems and this analysis from Ghana is potentially valuable in attracting global attention and thinking about emergency health systems capacity and reform in low and middle income countries. The solution to the "no bed syndrome" in Ghana requires reform of Ghana's emergency healthcare system that takes a whole system and integrated approach. All the components of the health system such as human resource, information systems, financing, equipment tools and supplies, management and leadership need to be examined and addressed together alongside health system values such as accountability, equity or fairness in the formulation, implementation, continuous monitoring and evaluation of policies and programs for system reform to expand and strengthen emergency healthcare system capacity and responsiveness. Despite the temptation to fall back on them as low hanging fruit, piecemeal and ad-hoc solutions cannot solve the problem.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".