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Record W4392595416 · doi:10.18192/aporia.v16i1.6947

Can mental health practice benefit from procedural justice theory? A critical analysis on the opportunities and pitfalls of procedural justice to address coercion and human rights issues in psychiatry

2024· article· en· W4392595416 on OpenAlexafffundvenue
Clara Lessard‐Deschênes, Pierre Pariseau‐Legault, Marie‐Hélène Goulet

Bibliographic record

VenueAporia · 2024
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité du Québec en OutaouaisUniversité de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsCoercion (linguistics)Procedural justiceMental healthEconomic JusticeHuman rightsPsychologyCriminologyPolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

The theory of procedural justice, derived from social psychology, is employed in numerous fields of study concerned with the quality of interactions involving individuals in positions of authority. Although this theory is increasingly cited for its potential to promote approaches aimed at mitigating the effects of psychiatric coercion and better respecting individuals' rights, empirical literature provides limited insights into how procedural justice could be translated into practice. It is important, therefore, to examine the theoretical and practical implications of such an orientation. Based on a critical analysis of existing literature, this article will discuss the potential contributions and limitations of procedural justice applied in the field of mental health and psychiatric nursing. Procedural justice has limitations regarding solutions for human rights violations in psychiatry. It nonetheless allows a focus on the quality of interactions with individuals in coercive contexts, in addition to considering the social and identity-related implications of psychiatric coercion.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.055
GPT teacher head0.433
Teacher spread0.379 · 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 designTheoretical or conceptual
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

Citations6
Published2024
Admission routes3
Has abstractyes

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