Mapping the gap: Misalignment between emergency care research and consensus priorities in the Western Cape
Bibliographic record
Abstract
Background: The Western Cape (WC) province of South Africa is one of the highest emergency care (EC) research-producing regions in Africa. In 2021, a consensus exercise with key stakeholders established 26 EC research priorities. This study aimed to confirm evidence gaps in relation to the priorities and assess the alignment between frontline knowledge needs and research output within the WC EC community. Methods: We developed an evidence map of all EC research published from the WC between January 2017 and December 2021 to describe the alignment of each publication with any previously established priority. Additional data were extracted from all studies that addressed one or more of these priorities. Results: = 41), including seven priority-aligned papers, were behind a pay wall. Most priority-aligned studies were observational (48%) or qualitative (23%), with only two systematic reviews and no experimental studies. Conclusion: Less than a quarter of recent EC research publications from the WC addressed established consensus priorities, confirming the existence of consensus evidence gaps and suggesting potential misalignment between research output and community-identified needs. Contribution: This study provides an evidence-based assessment of how well EC research in the WC reflects community-established priorities. The findings highlight the need for stronger alignment between research production and frontline knowledge needs to maximise impact and reduce research waste.
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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 | 0.182 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.036 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".