A scoping review of perceptions from healthcare professionals on antipsychotic prescribing practices in acute care settings
Bibliographic record
Abstract
BACKGROUND: Antipsychotic medications are frequently prescribed in acute care for clinical indications other than primary psychiatric disorders such as delirium. Unfortunately, they are commonly continued at hospital discharge and at follow-ups thereafter. The objective of this scoping review was to characterize antipsychotic medication prescribing practices, to describe healthcare professional perceptions on antipsychotic prescribing and deprescribing practices, and to report on antipsychotic deprescribing strategies within acute care. METHODS: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Web of Science databases from inception date to July 3, 2021 for published primary research studies reporting on antipsychotic medication prescribing and deprescribing practices, and perceptions on those practices within acute care. We included all study designs excluding protocols, editorials, opinion pieces, and systematic or scoping reviews. Two reviewers screened and abstracted data independently and in duplicate. The protocol was registered on Open Science Framework prior to data abstraction (10.17605/OSF.IO/W635Z). RESULTS: Of 4528 studies screened, we included 80 studies. Healthcare professionals across all acute care settings (intensive care, inpatient, emergency department) perceived prescribing haloperidol (n = 36/36, 100%) most frequently, while measured prescribing practices reported common quetiapine prescribing (n = 26/36, 76%). Indications for antipsychotic prescribing were delirium (n = 48/69, 70%) and agitation (n = 20/69, 29%). Quetiapine (n = 18/18, 100%) was most frequently prescribed at hospital discharge. Three studies reported in-hospital antipsychotic deprescribing strategies focused on pharmacist-driven deprescribing authority, handoff tools, and educational sessions. CONCLUSIONS: Perceived antipsychotic prescribing practices differed from measured prescribing practices in acute care settings. Few in-hospital deprescribing strategies were described. Ongoing evaluation of antipsychotic deprescribing strategies are needed to evaluate their efficacy and risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".