Meds on the Menu: The Covert Administration of Psychotropic Medication to Adult Inpatients Determined to be Decisionally-Incapable in Ontario's Psychiatric Settings
Bibliographic record
Abstract
Drawing on the fields of human rights and public health, this research explores the covert administration of medication: the concealment of medication in food or drink so that it will be consumed undetected. Adopting a rights-based approach, it explores multiple understandings of the impact of the practice on inpatients' rights-experiences. Relying on critical approaches, it also explores the practice's underlying socio-political-legal structures. The common themes of policies, protocols or guidelines that govern its practice in Ontario are identified. Focus groups and individual interviews were held with three groups of stakeholders (nurses, legal experts and psychiatrists), relying on fictional clinical scenarios. Few policies, protocols or guidelines govern the practice in Ontario's psychiatric settings. The practice impairs access to knowledge by patients and substitute decision-makers. It also precludes healthcare practitioners' access to information about side effects and underlying reasons for medication refusal. It may interfere with therapeutic relationships and patients' meaningful recovery as they transfer from hospital without knowledge of the fact of the covert medication. It may be characterized as autonomy restoring since patients may become capable of making treatment decisions after having received the medication surreptitiously. Covert medication reflects an inflexible approach to capacity determination; it is distinguishable from approaches that imagine capacity as able to be fostered with support. It is primarily concerned with the management of "risky" inpatients in the short-term. The practice relies on a faith that medication will be effective, deferring to medical decision-making. While covert medication is understood to have "something to do" with rights, there is confusion about how those rights play out on the ground. Institutional silences underlie and reinforce the practice. This research will support the development of effective, safe and appropriate approaches to treatment non-adherence that maximize patient dignity. Most pressing, this research concludes that the covert administration of medication warrants an overt discussion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".