Gaining Autonomy & Medication Management (GAM): new perspectives on well-being, quality of life and psychiatric medication
Bibliographic record
Abstract
Autonomous Medication Management (GAM) is an innovative approach developed in partnership with medication users. It takes their subjective experience into account and strives to place the individual at the center of pharmacological treatment in psychiatry with a view to improving well-being and quality of life. It creates spaces of open dialogue on the issue of medication amongst users, physicians and their family and friends. This article is derived from a research study and presents the principles, practices and main impacts of GAM on how people relate to their medications and the physicians who prescribe them. The major positive effects were the users' clearer understanding of their experience of taking psychiatric medication and their rights, the reduction or elimination of sudden and unsupervised treatment interruptions and the users' sense of having more control over their treatment. It includes inner experience and life, an improved relationship with professionals and space for negotiation with the physician and, lastly, changes to prescriptions that significantly improved well-being and recovery. The distinguishing features of GAM are described and compared with other approaches, giving a voice to people who take medication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".