MétaCan
Menu
Back to cohort
Record W4415819555 · doi:10.7202/1121335ar

MAiD, Mental Disorders, and Vulnerability: How Common Responses to Vulnerability Concerns are Inadequate

2025· article· en· W4415819555 on OpenAlexaffvenue
Loughran Butcher

Bibliographic record

VenueCanadian Journal of Bioethics · 2025
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsCarleton University
Fundersnot available
KeywordsVulnerability (computing)Stigma (botany)PhraseMental illnessPoint (geometry)Vulnerability assessment

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.430
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of BioethicsSame topicHealthcare Decision-Making and RestraintsFrench-language works237,207