<scp>NOTICE</scp> ‐ <scp>ED</scp> : Nurse or Technician Insights Into Cognitive Evaluations in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Several strategies have been proposed to increase chronic cognitive impairment (CI) screening in the emergency department (ED). Our goal was to assess the feasibility and acceptability of implementing specific CI screening tools and strategies in the ED from an ED registered nurse and technician perspective. METHODS: We performed a qualitative study using semi-structured interviews with a purposive sample of ED nurses and ED technicians (EDTs). Participants worked at an urban academic hospital and were interviewed between November 2023 and March 2024. Interviews assessed participants' opinions on the feasibility and acceptability of CI screening and the use of machine learning (ML) tools to identify high-risk patients for targeted CI screening, tablet-based screenings, and two validated CI screenings: the Ottawa 3DY (O3DY) and Short Blessed Test (SBT). We used the Consolidated Framework for Implementation Research (CFIR) to develop our interview guide and performed a rapid analysis with deductive and inductive codes based on CFIR constructs. RESULTS: Four major themes related to CI screening tools arose: (1) Benefits of CI screening; (2) feasibility of integrating screening tools into existing workflows; (3) professional role limitations; and (4) implementation requirements. Participants perceived CI screening as important for allocating limited ED resources. Shorter, less specific testing, including the O3DY, was seen as feasible during triage, while longer, more specific screening, including the SBT, was seen as more feasible in roomed care areas. Both ED nurses and EDTs identified the need for electronic health record tools and dedicated screening teams to facilitate implementation. CONCLUSION: ED nurses and EDTs support chronic CI screening if screening techniques and clinical teams can be optimized to make workflows feasible.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".