MétaCan
Menu
Back to cohort
Record W4309124668 · doi:10.13162/hro-ors.v10i2.4821

Structured Intervention Units and Mental Health in Canadian Federal Prisons: A Policy Assessment of Bill C-83

2022· article· en· W4309124668 on OpenAlexaffvenueabout
Lamiah Adamjee

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill University
Fundersnot available
KeywordsCharterGovernment (linguistics)PrisonMental healthContext (archaeology)Political scienceHuman rightsIntervention (counseling)Public administrationMental Health ActBill of rightsHuman servicesLawMedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

On 21 June 2019, the federal government passed Bill C-83: An Act to amend the Corrections and Conditional Release Act and another Act with the primary intention of eliminating the use of administrative and disciplinary segregation in Canadian federal prisons and replacing the practice with structured intervention units. This new correctional model would serve as a way to separate unsafe inmates while ensuring they receive appropriate mental health services, increased time outside of their units, and meaningful human contact. The conception of Bill C-83 was largely a result of a history of widespread criticisms that administrative segregation infringed on human rights, posed significant mental health concerns, and violated the Canadian Charter of Rights and Freedoms. While the reform mitigated immediate pressure on the federal government to act, several advocacy groups, legal experts, politicians, and other stakeholders voiced negative opinions and indicated the shortcomings of the new units and the impact they will have on the health of prisoners. Bill C-83 has the potential to prompt significant change in the context of prison health reform, but this will be dependent on the federal government’s coordination with Correctional Services Canada.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.235
GPT teacher head0.559
Teacher spread0.323 · 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.

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

Citations3
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueHealth Reform Observer - Observatoire des Réformes de SantéSame topicHealth Policy Implementation ScienceFrench-language works237,207