Attitudes Driving Regional Differences in Long-Acting Injectable Antipsychotic Utilization for Schizophrenia among Healthcare Professionals, Patients, and Caregivers (ADVANCE): Results from a Multinational Survey Study
Bibliographic record
Abstract
Abstract Background Long-acting injectable (LAI) antipsychotics improve adherence and reduce schizophrenia relapse rates vs oral antipsychotics (OAs) but remain underused. The ADVANCE study explored country-level differences in LAI use among healthcare professionals (HCPs), patients, and caregivers, to identify drivers of LAI use. Study Design ADVANCE included participants from Australia, Canada, China, Germany, Israel, South Korea, Spain, and the United States. Eligible HCPs spent ≥25% of their time in direct patient care, managed an adult population of whom ≥10% have schizophrenia, and treated patients prescribed LAIs. Patients aged ≥18 years and caregivers of adults living with schizophrenia who had tried/been offered an LAI were included. Participants completed a 30-minute survey. Study Results Systemic factors reported by HCPs (n = 791) associated with higher LAI use included being a physician vs nonphysician, having Hispanic/Latino/Spanish ethnicity, managing more adult patients with schizophrenia, and having more staff and nurse support. Nonadherence to OAs was the main HCP-reported reason for LAI recommendation. Patient characteristics, lack of available LAIs corresponding to OAs, and perceptions of patients’ behavior were top reasons HCPs would not recommend an LAI. For patients (n = 470), symptom improvement and HCP recommendation, and for caregivers (n = 381), ease of injections, reduced hospitalizations, and fewer side effects were the main reasons for LAI acceptance. The top reason patients and caregivers declined LAIs was concern about side effects. Conclusions Results from ADVANCE demonstrate that systemic and attitudinal factors influence LAI use by HCPs, and these factors vary by country. Enhancing HCP-patient communication may improve LAI acceptance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".