Achieving better outcomes for schizophrenia patients in Hong Kong: Strategies for improving treatment adherence
Bibliographic record
Abstract
Recent research on second-generation long-acting injectable antipsychotics (LAI SGAs) has proven its effectiveness in minimizing medication nonadherence problem and reducing relapses. Administered by medical professionals, making quick detection of nonadherence possible, long-acting injectable antipsychotics (LAIs) facilitate immediate intervention and recovery process, and thus are favored by psychiatrists. Despite a higher initial cost with LAIs, the subsequent schizophrenia-related health costs for hospitalizations and outpatients are greatly reduced. With reference to guidelines published by psychiatric associations around the globe, this article looks at scenarios in Hong Kong on the management of severe mentally ill patients with regard to the use of a host of psychosocial interventions as well as LAI SGAs as a preferable treatment. In particular, it examines the benefits of using LAI SGAs for Hong Kong patients who demonstrated high nonadherence treatment rates due to their social environment. It assesses the rationale behind the early usages of LAI SGAs, which help to provide better recovery outcomes for patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".