Short communication: Prevalence of long-acting injectable antipsychotic use in Canadian early intervention services for psychosis
Bibliographic record
Abstract
The use of long-acting injectable (LAI) antipsychotic drugs for psychotic disorders in Canada has been historically low compared to other jurisdictions despite advantages of LAIs in improving medication adherence and preventing relapse. In response, treatment recommendations were developed in 2013 by the Canadian Consortium for Early Intervention in Psychosis and other Canadian provincial expert groups. The impact of these guidelines needed to be assessed. To document practices in LAI use in early intervention services (EIS) for psychosis, Canadian EIS were surveyed in 2016 (n = 18) and 2020 (n = 12). Trends and descriptive information were examined using repeated cross-sectional survey data. Eight EIS responded to surveys at both time points allowing for longitudinal comparisons. Outcomes of interest included i) LAI use frequency, ii) timing of LAI starts, and iii) factors influencing LAI use. Cross-sectional analysis identified a significant increase in overall LAI usage (24.7% in 2016; 35.1% in 2020). Longitudinal analysis indicated that patients in the second program year saw the greatest increase in LAI use between 2016 and 2020 (25.6% vs. 36.1%), especially among patients under community treatment orders (65.5% vs. 81.5%). Results support increases in LAI use over time, accessibility, awareness, and increasing comfortability among Canadian clinicians.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".