Understanding the Health System Conditions Affecting the Use of Long-Acting Injectable Antipsychotics in the Treatment of Schizophrenia in Clinical Practice: A US Healthcare Provider Survey
Bibliographic record
Abstract
Purpose: To describe factors that enable the routine use of long-acting injectable antipsychotics (LAIs) for appropriate patients in the current clinical practice, including changes in LAI prescribing due to the COVID-19 pandemic and expectations for prescribing in 2021 in the United States (US). Methods: Frequent LAI prescribers recruited from a nationwide panel in 2020 completed an online survey regarding practice characteristics, perspectives on healthcare system conditions enabling routine use of LAIs, and prescribing patterns and changes in patterns during the COVID-19 pandemic. Results: Of 408 prescribers who completed the survey, 77.7% were physicians and 59.1% had ≥10 years of psychiatry practice. More than half of frequent prescribers (57.1%) reported treating >20% of their patients with schizophrenia with LAIs. The American Psychiatric Association (APA) guideline was followed by 64.0% of prescribers. Most prescribers identified poor adherence to antipsychotics as a circumstance when LAIs are recommended (94.9%) and patient/caregiver involvement in treatment decisions as a key factor impacting the decision to prescribe LAIs (97.3%). Most prescribers reported that LAI prescribing rates were unchanged in 2020 (59.8%). Similar proportions of prescribers expected no change (44.1%) or an increase (42.9%) in LAI prescribing rates in 2021. The number of patients followed, cost of treatment, and availability of staff to administer LAIs were the main driving factors identified by prescribers expecting an increase in LAI prescribing rates. Conclusion: LAIs were commonly recommended to patients with poor adherence, and patient/caregiver involvement was an important factor affecting prescribers' treatment decisions. LAI prescribing rates remained unchanged during the COVID-19 pandemic in 2020.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".