Exploring Acute Care Nurses' Decision-Making in Psychotropic PRN Use in Hospitalized People with Dementia
Bibliographic record
Abstract
This qualitative descriptive study used semi-structured interviews to explore how acute care nurses decide to administer “as needed” (PRN) psychotropic medication to hospitalized people with dementia. Eight nurses from three acute care units in a large tertiary hospital in Western Canada were interviewed using a semi-structured interview guide. Conventional content analysis yielded three themes that reflect nurses’ decision-making related to administering PRNs to hospitalized people with dementia: Legitimizing Control which involved medicating undesirable behaviors to promote the nurses’ perceptions of safety; Making the Patient Fit to maintain routine and order; and Future Telling involved pre-emptively medicating to prevent undesirable behaviors from escalating. Nurses provided little to no mention of assessing for physical causes contributing to behaviors; notably, not one participant mentioned pain. PRNs were seen as a reasonable alternative to physical restraints and were frequently used. Additionally, organizational and unit routines greatly influenced nurses’ decision-making. These findings provide an initial understanding into some of the ways nurses make decisions to administer PRN medications and may inform prescribing practices. More research is needed to better understand the complexities of nurses’ decision-making which will assist in the development of interventions for nursing practice.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".