Analysis of non‐pharmacological interventions attempted prior to pro re nata medication use
Bibliographic record
Abstract
The aim of the present study was to elucidate what non-pharmacological interventions are applied by nursing staff prior to the administration of psychotropic pro re nata (PRN) medication. Best practices would instruct clinical staff to provide non-pharmacological strategies, such as de-escalation and skills coaching, as the first response to patient distress, anxiety, or agitation. Non-pharmacological strategies might be safer for patients, promote more collaborative relationships, and facilitate greater skills development for managing symptoms. The literature has highlighted that poor documentation of pre-PRN administration interventions has limited our understanding of this practice, but evidence suggests that when this information is available, non-pharmaceutical approaches are not being attempted in the majority of cases. This is troubling given that, while clinically appropriate in some instances, PRN have been subject to criticism and lack critical evidence to support their use. The current study is a continuation of our previous work, which examined the reason, frequency, documentation, and outcome (e.g. effectiveness, side-effects) of PRN medication use at our facility. A chart review was conducted to understand what happens prior to the administration of PRN medication at our facility across all inpatient units over the course of 3 months. Results support previous findings that non-pharmacological interventions are poorly documented by front-line staff and are seemingly used infrequently. The use of these interventions differs by patient presentation (e.g. agitation, insomnia), and most often include supportive measures. The findings suggest that both documentation and intervention practices of nursing staff require further investigation and adjustment to align with best practices.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".