Updated framework on quality and safety in emergency medicine
Bibliographic record
Abstract
OBJECTIVES: Quality and safety of emergency care is critical. Patients rely on emergency medicine (EM) for accessible, timely and high-quality care in addition to providing a 'safety-net' function. Demand is increasing, creating resource challenges in all settings. Where EM is well established, this is recognised through the implementation of quality standards and staff training for patient safety. In settings where EM is developing, immense system and patient pressures exist, thereby necessitating the availability of tiered standards appropriate to the local context. METHODS: The original quality framework arose from expert consensus at the International Federation of Emergency Medicine (IFEM) Symposium for Quality and Safety in Emergency Care (UK, 2011). The IFEM Quality and Safety Special Interest Group members have subsequently refined it to achieve a consensus in 2018. RESULTS: Patients should expect EDs to provide effective acute care. To do this, trained emergency personnel should make patient-centred, timely and expert decisions to provide care, supported by systems, processes, diagnostics, appropriate equipment and facilities. Enablers to high-quality care include appropriate staff, access to care (including financial), coordinated emergency care through the whole patient journey and monitoring of outcomes. Crowding directly impacts on patient quality of care, morbidity and mortality. Quality indicators should be pragmatic, measurable and prioritised as components of an improvement strategy which should be developed, tailored and implemented in each setting. CONCLUSION: EDs globally have a remit to deliver the best care possible. IFEM has defined and updated an international consensus framework for quality and safety.
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 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 | 0.166 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.011 | 0.017 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".