Preventing avoidable incidents leading to a presentation to the emergency department (ED) by older adults with cognitive impairment: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Older adults with cognitive impairment represent a large portion (21-42%) of people (65+) who consult at an emergency department (ED). Because this sub-group is at higher risk for hospitalisation and mortality following an ED visit, awareness about 'avoidable' incidents should be increased in order to prevent presentations to the ED due to such incidents. This study aims to synthetise the actual knowledge related to 'avoidable' incidents (ie, traumatic injuries, poisoning and other consequences of external causes) (WHO, 2016) leading to ED presentations in older people with cognitive impairment. METHODOLOGY AND ANALYSIS: A scoping review will be performed. Scientific and grey literature (1996-2016) will be searched using a combination of key words pertaining to avoidable incidents, ED presentations, older adults and cognitive impairment. A variety of databases (MEDLINE, CINAHL, Ageline, SCOPUS, ProQuest Dissertations/theses, EBM Reviews, Healthstar), online library catalogues, governmental websites and published statistics will be examined. Included sources will pertain to community-dwelling older adults presenting to the ED as a result of an avoidable incident, with the main focus on those with cognitive impairment. Data (eg, type, frequency, severity, circumstances of incidents, preventive measures) will be extracted and analysed using a thematic chart and content analysis. DISCUSSION AND DISSEMINATION: This scoping review will provide a picture of the actual knowledge on the subject and identify knowledge gaps in existing literature to be filled by future primary researches. Findings will help stakeholders to develop programmes in order to promote safe and healthy environments and behaviours aimed at reducing avoidable incidents in seniors, especially those with cognitive impairment.
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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 | 0.105 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".