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
Bibliographic record
Abstract
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 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 | 0.051 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.116 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".