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Record W4366332046 · doi:10.1007/s11606-023-08042-5

Facilitators and Barriers Influencing Antipsychotic Medication Prescribing and Deprescribing Practices in Critically Ill Adult Patients: a Qualitative Study

2023· article· en· W4366332046 on OpenAlexaffabout
Natalia Jaworska, Karla D. Krewulak, Emma Schalm, Daniel J. Niven, Zahinoor Ismail, Lisa Burry, Jeanna Parsons Leigh, Kirsten M. Fiest

Bibliographic record

VenueJournal of General Internal Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSinai Health SystemDalhousie UniversityUniversity of TorontoUniversity of CalgaryHotchkiss Brain InstituteAlberta Health Services
Fundersnot available
KeywordsDeprescribingMedicineAntipsychoticDeliriumThematic analysisContext (archaeology)Qualitative researchNursingMedical prescriptionPharmacistHealth carePsychiatryPolypharmacySchizophrenia (object-oriented programming)Intensive care medicinePharmacy

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.036
GPT teacher head0.378
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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