Nonmedical problems among older adults visiting the emergency department for low acuity conditions: A prospective multicentre cohort study
Bibliographic record
Abstract
Background: Data on the predictors of nonmedical problems (NMP) in older adults attending the emergency department (ED) for low acuity conditions is lacking and could help rapid identification of patients with NMP and integration of these needs into care planning. Objectives: To determine the prevalence and predictors of NMP among older adults attending EDs for low acuity conditions. Methods: Prospective cohort study in eight EDs (May-August 2021). We included cognitively intact ≥65 years old adults assigned a low triage acuity (3-5) using the CTAS. A questionnaire focusing on 11 NMP was administered. We used multiple logistic regression to identify predictors of NMP. Results: Among the 1,061 participants included, the mean age was 77.1 ± 7.6, majority were female, and 41.6 % lived alone. At least one NMP was reported by 704 persons. Prevalence of each NMP: outdoor (41.1 %) and indoor (30.2 %) mobility issues, difficult access to dental care (35.1 %), transportation (4.1 %) and medication (5.4 %), loneliness (29.5 %), food insecurity (10.3 %), financial difficulties (9.5 %), unsafe living situation (4.1 %), physical/psychological violence (3.4 %), and abuse/neglect (3.3 %). Predictors of NMP were: age (OR 1.04 for each additional year), living alone (OR 2.20), pre-existing mental health conditions (OR 3.12), heart failure (OR 1.42), recent surgery/admission (OR 1.75), memory decline (OR 2.76), no family physician (OR 1.74) and consulting for a fall/functional decline (OR 2.48). Conclusions: Nonmedical problems are frequent among older adults. We need to implement holistic ED processes that integrate these problems into care planning.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".