Facilitators and Barriers Influencing Antipsychotic Medication Prescribing and Deprescribing Practices in Critically Ill Adult Patients: a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Antipsychotic medications do not alter the incidence or duration of delirium, but these medications are frequently prescribed and continued at transitions of care in critically ill patients when they may no longer be necessary or appropriate. OBJECTIVE: The purpose of this study was to identify and describe relevant domains and constructs that influence antipsychotic medication prescribing and deprescribing practices among physicians, nurses, and pharmacists that care for critically ill adult patients during and following critical illness. DESIGN: We conducted qualitative semi-structured interviews with critical care and ward healthcare professionals including physicians, nurses, and pharmacists to understand antipsychotic prescribing and deprescribing practices for critically ill adult patients during and following critical illness. PARTICIPANTS: Twenty-one interviews were conducted with 11 physicians, five nurses, and five pharmacists from predominantly academic centres in Alberta, Canada, between July 6 and October 29, 2021. MAIN MEASURES: We used deductive thematic analysis using the Theoretical Domains Framework (TDF) to identify and describe constructs within relevant domains. KEY RESULTS: Seven TDF domains were identified as relevant from the analysis: Social/Professional role and identity; Beliefs about capabilities; Reinforcement; Motivations and goals; Memory, attention, and decision processes; Environmental context and resources; and Beliefs about consequences. Participants reported antipsychotic prescribing for multiple indications beyond delirium and agitation including patient and staff safety, sleep management, and environmental factors such as staff availability and workload. Participants identified potential antipsychotic deprescribing strategies to reduce ongoing antipsychotic medication prescriptions for critically ill patients including direct communication tools between prescribers at transitions of care. CONCLUSIONS: Critical care and ward healthcare professionals report several factors influencing established antipsychotic medication prescribing practices. These factors aim to maintain patient and staff safety to facilitate the provision of care to patients with delirium and agitation limiting adherence to current guideline recommendations.
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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 | 0.009 | 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 | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".