Pharmacist Administration of Long-Acting Injectable Antipsychotics to Community-Dwelling Patients: A Scoping Review
Bibliographic record
Abstract
Long-acting injectable antipsychotics (LAIAs) have demonstrated positive outcomes for people with serious mental illnesses. They are underused, and access to LAIAs can be challenging. Pharmacies could serve as suitable environments for LAIA injection by pharmacists. To map and characterize the literature regarding the administration of LAIAs by pharmacists, a scoping review was conducted. Electronic-database searches (e.g., PsycINFO, Ovid Medline, Scopus, and Embase) and others including ProQuest Dissertations & Theses Global and Google, were conducted. Citation lists and cited-reference searches were completed. Zotero was used as the reference-management database. Covidence was used for overall review management. Two authors independently screened articles and performed full-text abstractions. From all sources, 292 studies were imported, and 124 duplicates were removed. After screening, 13 studies were included for abstraction. Most articles were published in the US since 2010. Seven studies used database and survey methods, with adherence and patient satisfaction as the main patient-outcomes assessed. Reporting of pharmacists' and patients' perspectives surrounding LAIA administration was minimal and largely anecdotal. Financial analyses for services were also limited. The published literature surrounding pharmacist administration of LAIAs is limited, providing little-to-no guidance for the development and implementation of this service by others.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".