Staff and patient accounts of <scp>PRN</scp> medication administration and non‐pharmacological interventions for anxiety
Bibliographic record
Abstract
Most psychiatric inpatients will receive psychotropic PRN medication during their hospital stay for agitation, anxiety, and/or insomnia. While helpful in some cases, caution is warranted with regard to PRN use due to inherent risks of additional medication; therefore, experts advise that non-pharmacological interventions should be attempted first where indicated. However, research to date highlights that, in practice, non-pharmaceutical approaches are attempted in a minority of cases. While some information is known about the practice of PRN administration and the use of and barriers to implementing non-pharmacological interventions for treating acute psychiatric symptoms, full understanding of this practice is hampered by poor or altogether missing documentation of the process. This study used interviews with patients and staff from two psychiatric hospitals to collect first-person accounts of administering PRN medication for anxiety, thereby addressing the limitations of relying on documented notation found in previous research. Our results indicate that nurses are engaging in non-pharmacological interventions more often than had previously been captured in research. However, the types of strategies suggested are not typically evidence based and further, only happening approximately half the time. The barriers to providing such care are centred on two main beliefs about client choice and efficacy of these non-medical strategies. Implications for research and practice are discussed.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".