Guidance we can trust? The status and quality of prehospital clinical guidance in sub-Saharan Africa: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Prehospital care is integral in addressing sub-Saharan Africa's (SSA) high injury and illness burden. Consequently, robust, high-quality prehospital guidance documents are needed to inform care. These guidance documents include, but are not limited to, clinical practice guidelines (CPGs), protocols and algorithms that are contextually appropriate for SSA. However, SSA prehospital guidance mostly originates from the 'Global North,' with limited guidance for Africa by Africans. To strengthen prehospital clinical practice in SSA, we described and appraised all prehospital SSA guidance documents informing clinical decision making. METHODS: We conducted a scoping review of prehospital-relevant guidance documents, including CPGs, algorithms, protocols and position statements originating from SSA. We performed a comprehensive literature search in various databases (PUBMED and SCOPUS), guideline clearing houses (Scottish Intercollegiate Guidelines Network, Trip, and Guidelines International Network), journals, various forms of grey literature and contacted experts. Guidance document screening and data extraction was done independently, in duplicate and reviewed by a third author. Guidance quality was then determined using the AGREE II tool and data were analysed using simple descriptive statistics. RESULTS: We included 51 guidance documents from 13 countries across SSA after screening 2320 potential documents. The majority of guidance documents lacked an evidence foundation, made recommendations based on expert input, and were predominantly end-user presentations such as algorithms or protocols. Overall, reporting quality was poor, specifically for critical domains such as rigour of development; however, clarity of presentation was generally strong. Guidance topics were focused around resuscitation and common diseases (both communicable and non-communicable) with major gaps identified across a variety of topics; such as mental health for example. CONCLUSION: The majority of prehospital clinical guidance from SSA provides clinicians with excellent ready to use end-user material. Conversely, most of the guidance documents lack an appropriate evidence foundation and fail to transparently report the guidance development process, highlighting the need to strengthen and build guideline development capacity to promote the transition from eminence-based to evidence-based guidance for prehospital care in SSA. Guideline developers, professional societies and publishers need to be aware of international and local guidance document development and reporting standards in order to produce guidance we can trust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".