MAiD, Mental Disorders, and Vulnerability: How Common Responses to Vulnerability Concerns are Inadequate
Bibliographic record
Abstract
The concept of vulnerability is bandied about frequently in ongoing debates about medical assistance in dying (MAiD) and who should be eligible, but is often used as a broad catch-all phrase to capture some sort of risk or concern that people have. This imprecise usage obfuscates the concerns that opponents to MAiD have about expansion to include those suffering from mental disorders as the sole underlying condition. Since what is intended to be captured by the term ‘vulnerable’ is at times unclear, attempts to respond to or mitigate this vulnerability can miss the mark. Arguments from vulnerability against expanding access to MAiD point out social and/or systemic factors that may influence the choices of people living with mental disorders to access MAiD, such as lack of access to adequate care, stigma and discrimination, suicidality, and the correlation between mental disorders and low socio-economic status. However, the common response to concerns about vulnerability, made by those who argue for expansion, focus on highlighting current safeguards that are in place to ensure only those who are eligible for MAiD gain access. Under this view, vulnerability is determined by assessing individuals for eligibility. Those who cannot meet the eligibility criteria would not be permitted access. Yet, this entirely misses the concerns being raised that point to systemic or social sources of risk. Ensuring that the individuals who access MAiD meet the criteria is to ignore the reasons for accessing it in the first place.
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 | 0.062 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.118 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".