A first look at consistency of documentation across care settings during emergency transitions of long-term care residents
Bibliographic record
Abstract
BACKGROUND: Documentation during resident transitions from long-term care (LTC) to the emergency department (ED) can be inconsistent, leading to inappropriate care. Inconsistent documentation can lead to undertreatment, inefficiencies and adverse patient outcomes. Many individuals residing in LTC have some form of cognitive impairment and may not be able to advocate for themselves, making accurate and consistent documentation vital to ensuring they receive safe care. We examined documentation consistency related to reason for transfer across care settings during these transitions. METHODS: We included residents of LTC aged 65 or over who experienced an emergency transition from LTC to the ED via emergency medical services. We used a standardized and pilot-tested tracking tool to collect resident chart/patient record data. We collected data from 38 participating LTC facilities to two participating EDs in Western Canadian provinces. Using qualitative directed content analysis, we categorized documentation from LTC to the ED by sufficiency and clinical consistency. RESULTS: We included 591 eligible transitions in this analysis. Documentation was coded as consistent, inconsistent, or ambiguous. We identified the most common reasons for transition for consistent cases (falls), ambiguous cases (sudden change in condition) and inconsistent cases (falls). Among inconsistent cases, three subcategories were identified: insufficient reporting, potential progression of a condition during transition and unclear reasons for inconsistency. CONCLUSIONS: Shared continuing education on documentation across care settings should result in documentation supports geriatric emergency care; on-the-job training needs to support reporting of specific signs and symptoms that warrant an emergent response, and discourage the use of vague descriptors.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".