Understanding and Managing Treatment Adherence in Schizophrenia
Bibliographic record
Abstract
Schizophrenia is a chronic, debilitating and costly illness. The course of illness is often exacerbated by relapses which are associated with negative outcomes including rehospitalisation. The most important risk factor associated with relapse is medication nonadherence. Medication nonadherence is not specific to schizophrenia and is an issue across all of medicine. The objective of this paper is to present a narrative review which synthesizes the rates and predictors of medication nonadherence, as well as associated interventions, across schizophrenia, first episode psychosis and general medicine. Given the breadth of these topics, this paper does not aim to present a complete review of the data but rather a concise synthesis of several lines of research in order to provide a general framework for approaching this important topic. Overall, this paper identifies that rates and risk factors of nonadherence in schizophrenia are similar to those reported in general medicine. Rates of adherence are estimated at 50% for both. Predictors of nonadherence were also quite similar between various illnesses, with lack of insight, poor family support and substance abuse often being highlighted. Well studied approaches of improving adherence include simplifying medication regimens, psychoeducation, engaging family support and use of long-acting injectable antipsychotics. Emerging interventions included text-message reminders, financial incentives and MyCite technology. Additionally, several evidence based interventions were identified in general medicine that may have applicability in schizophrenia and first episode psychosis. Lastly, avenues of future research were identified including the need to further characterize the dichotomy between adherence, partial adherence and nonadherence.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".