Consensus‐based policy recommendations for geriatric emergency care
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to establish policy recommendations to address service and care delivery challenges facing hospital emergency departments (EDs) responding to the needs of increasing numbers of older adults. DESIGN/METHODOLOGY/APPROACH: The consensus development process used an international expert interdisciplinary panel, convened at an international conference. Following a round table discussion and think-tank session, a nominal group method with constant comparative analysis and coding techniques was used to identify policy recommendations. Two rounds of electronic input followed the face-to-face meeting to reach consensus on priority ranking of the policy recommendations. Findings underwent an external review by four independent experts. FINDINGS: A total of seven categories of policy recommendations were developed: education, integration and coordination of care, resources, ED physical environment, evidence-based practice, research and evaluation, and advocacy. RESEARCH LIMITATIONS/IMPLICATIONS: The consensus development process did not include a systematic literature review on the topic. However, participants included experts in their disciplines. PRACTICAL IMPLICATIONS: The recommendations may assist administrators, policy makers, clinicians, and researchers on future directions for improving emergency care and service delivery for older adults. ORIGINALITY/VALUE: The paper describes the process and results of a consensus development activity for ED care and services of older adults.
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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 | 0.336 | 0.506 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".