Antipsychotic prescribing practices and patient, family member and healthcare professional perceptions of antipsychotic prescribing in acute care settings: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Antipsychotic medications are commonly prescribed off-label in acutely ill patients for non-psychiatric clinical indications such as delirium or insomnia. New prescription initiation of antipsychotics in acute care settings increases the proportion of patients discharged home on antipsychotics without approved clinical indication. Long-term use of antipsychotics is associated with increased risk of sudden cardiac death, falls and cognitive impairment. An understanding of acute care off-label antipsychotic prescribing practices and healthcare professional, patient and family perceptions related to antipsychotic prescribing and deprescribing is necessary to facilitate in-hospital deprescribing initiatives. METHODS AND ANALYSIS: We present the protocol for a scoping review following the methodology proposed by Arksey and O'Malley and the Scoping Review Methods Manual by the Joanna Briggs Institute. We will search five databases including MEDLINE, EMBASE, CINAHL, PsycINFO and Web of Science from inception to 3 July 2021 (ie, planned search date). We will include both peer-reviewed and non-peer-reviewed qualitative and quantitative studies to identify antipsychotic prescribing practices, and to describe healthcare professional, patient and family perceptions towards antipsychotic prescribing and deprescribing in the acute care setting. Protocols, systematic and scoping reviews will be excluded. Two reviewers will calibrate and perform study screening and data abstraction for quantitative and qualitative outcomes of eligible studies. Quantitative outcomes will include study identifiers, demographics and descriptive statistics of antipsychotic prescribing practices. Qualitative synthesis describing perceptions on antipsychotic prescribing practices will include deductive thematic analysis with mapping of themes to the domains of the Theoretical Domains Framework, a 14-domain behaviour and behaviour change framework. ETHICS AND DISSEMINATION: No ethical approval will be required for this study as only data from published studies in which informed consent was obtained by primary investigators will be retrieved and analysed. The results of this scoping review will inform integrated knowledge translation initiatives aimed at in-hospital antipsychotic medication deprescribing.
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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 | 0.089 | 0.091 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".