DELIRIUM SUPERIMPOSED ON DEMENTIA IN POST-ACUTE CARE: NURSE DOCUMENTATION OF SYMPTOMS AND INTERVENTIONS
Bibliographic record
Abstract
Abstract Delirium is common in older adults and across settings of care, including post-acute care (PAC). Nurses have an important role in identifying, preventing and managing delirium. Even though best practice guidelines highlight the need to accurately document delirium and to deliver non-pharmacological, nurse-driven interventions, it is unclear how this is done in PAC. The aim of this research was two-fold: 1) to describe how nurses document DSD symptoms in PAC nursing notes and 2) to determine if appropriate non-pharmacological nursing interventions are included in their documentation when DSD is present. The sample (N=281) was drawn from a large, single-blinded randomized controlled trial (Recreational Stimulation For Elders As A Vehicle To Resolve DSD (Reserve-DSD) across 8 facilities. Participants tended to be white, female, and had a high-school education. A total of 115 participants (40.6%) had full delirium per the CAM upon admission to PAC, while the remainder 168 (59.4%) had subsyndromal delirium. All had a baseline of dementia. Symptoms of ‘Confusion or Acute Confusion’ were reported for more than 50% of patients. Approximately 90% of had the symptom ‘Confusion or Acute Confusion’ documented and this was also the most commonly documented symptom for which a nursing-driven intervention was provided. Overall delirium symptoms and interventions were poorly documented by nurses. Implications for future research and practice include understanding how the pandemic and subsequent resource deprivations impacted delirium documentation and intervention in this setting. Also there is a need for expanded nurse and other healthcare provider education.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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".