The Use of <i>Pro Re Nata</i> or <i>Statim</i> Medications for Behavioral Control: A Summary of Experience at a Tertiary Care Children's Mental Health Center
Bibliographic record
Abstract
The present study aims to identify patterns for use of medication given pro re nata (PRN or "on an as needed [preordered] basis") or statim (STAT [a new order] or "at once, immediately") and their efficacy in controlling aggressive behavior in the mental health (MH) services environment. PRN and STAT medication data were combined and referred to as PRN throughout this article, as the data were not collected in a manner required to differentiate between PRN and STAT medication administration. Analyzed data were extracted from the clinical records of a sample of children and youth admitted for the first time to a tertiary MH center. MH Program patients (characterized by at least one Axis I psychiatric diagnosis [Axis I group]) were compared to Dual Diagnosis Program patients (characterized by an Axis I diagnosis in addition to an Axis II diagnosis of mental retardation [Axis II group]). Age, gender, Program (Axis I or II group), and the length of stay for treatment produced significant differences in the use of PRNs between the two groups. Further, the study investigated the precipitating factors leading to use of PRNs, in conjunction with the level of supervision and the de-escalation techniques used to avoid the use of PRNs. Axis I patients were more likely to endanger others, whereas Axis II patients were more likely to endanger themselves. Both groups of patients demonstrated a need for an increased level of supervision prior to the crisis. Olanzapine, chlorpromazine, and lorazepam were effective in calming patients and preventing further aggressive outbursts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".