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Record W4386928778 · doi:10.7202/1105663ar

Telepsychiatry and mental health equity in correctional facilities: Legal opportunities and challenges

2023· article· en· W4386928778 on OpenAlexaffvenueabout
Dimitri Patrinos

Bibliographic record

VenueLex Electronica · 2023
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTelepsychiatryMental healthEquity (law)Health careMedicineMental health careNursingPublic relationsTelemedicinePsychiatryPolitical scienceLaw

Abstract

fetched live from OpenAlex

Lack of access to mental health care in Canadian correctional facilities is a serious and longstanding issue. Telepsychiatry, which entails the usage of information and communications technologies to provide remote mental health care to patients, has been demonstrated to be an effective model of mental health care provision in correctional facilities. The right to health care, including mental health care, of inmates is recognized in both international and domestic law. However, mental health conditions remain suboptimal in Canadian correctional facilities and are far below the standards which exist in the general community, leading to significant mental health disparities for inmates. Telepsychiatry can be viewed as a vector for increasing mental health equity in the correctional system and provides a promising opportunity for correctional facilities to meet their legal obligations to provide inmates with health care, including mental health care. This article explores the legal frameworks governing the provision of mental health care services in Canadian correctional facilities and highlights the role telepsychiatry can play in the fulfillment of these legal frameworks. It also explores the legal challenges facing the implementation of telepsychiatry in correctional facilities. Ultimately, despite these challenges, it argues that telepsychiatry should be more widely implemented in correctional facilities to ensure mental health equity for inmates.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.149
GPT teacher head0.419
Teacher spread0.270 · 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 designNot applicable
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

Citations0
Published2023
Admission routes3
Has abstractyes

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