Nurses’ decision‐making related to administering as needed psychotropic medication to persons with dementia: an empty systematic review
Bibliographic record
Abstract
Behavioural and psychological symptoms of dementia occur in approximately 75% of people with dementia admitted to acute care. Acute care nurses' decision-making regarding administering 'as needed' (pro re nata or PRN) psychotropic medications to persons with dementia are not well understood. This is an important clinical concern because 'as needed' medications are given at the discretion of the nurse. A comprehensive, systematic search and screen for studies that explored nurses' decision-making related to administering as needed psychotropic medication to persons with dementia in acute care settings was conducted. No studies that reported nurses' decision-making related to administration of as needed psychotropic medications to hospitalized persons with dementia were identified. In light of this, we present a discussion based on a narrative review of what is known on this topic from other settings, based on papers found in our original review. We will briefly explore what is needed in future research to address the gap in knowledge about nurse' decision-making related to administering as needed psychotropic medications. IMPLICATIONS FOR PRACTICE: Research is needed to understand and inform the decision-making process in the administration of as needed psychotropic medications to hospitalized persons with dementia.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".