How ethical is the use of financial incentives to promote medication adherence among patients with serious mental illness? - a scoping review of the literature
Bibliographic record
Abstract
Poor adherence to medication treatment in those with serious mental illness is common. Typical solutions and consequences in Western, formal psychiatric care settings often involve coercive measures such as involuntary hospital admissions and treatment, and out-patient commitments, with serious infringements on civil liberty. We examine the relatively unfamiliar, controversial and under-explored area of using financial incentive (FI) to enhance medication adherence in this population through a comprehensive scoping literature review, focusing on the ethical aspects of this practice. From nineteen qualified papers, we identified seven key themes of ethical “concerns,” and opposing arguments on whether FI is: (1) itself a form of coercion affecting autonomy; (2) leading to erosion of a proper consent process; (3) causing indirect and unintended harm; (4) worsening multiple levels of stigma related to mental illness; (5) associated with loss of intrinsic motivation of treatment; (6) associated with misuse of the fund itself; and (7) potentially leading to negative impact on therapeutic relationships. The ethics review shows a moderate receptivity to the use of FI in some specific and individualized circumstances; exceptions for those with poor insight and lacking treatment capacity are particularly relevant in order to avoid other forms of coercion. Additional highlights of the review include the optimal amount, duration, scope, and target population of the FI, and need for rigorous attention to psychoeducation, process, therapeutic relationships, stigma, and not lose sight of ultimately aiming to improve on patients’ insight, autonomy, quality of life and intrinsic motivation in this controversial approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".