Factors Associated with Perceived Coercion in Adults Receiving Psychiatric Care: A Scoping Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Perceived coercion has been associated with significant negative outcomes, including service avoidance and psychological distress. Despite growing interest, no recent comprehensive review has mapped the full range of factors influencing this experience. This scoping review aimed to synthesize and present the state of knowledge on the factors associated with perceived coercion by adults receiving psychiatric care. METHODS: Following the Joanna Briggs Institute methodology, a systematic search of five databases and grey literature was conducted for publications from 1990 to 2025 in English and French. A total of 143 sources were included and thematically analyzed. Consultation with experts and individuals with lived experience enriched the interpretation of findings. RESULTS: Five categories of factors were identified: individual, clinical, relational, legal, and structural. Relational and legal factors were most consistently associated with perceived coercion, while individual and clinical factors showed inconsistent findings. Structural influences were underexamined but significantly shaped the experiences of the individuals receiving care. CONCLUSIONS: Perceived coercion arises from a complex dynamic of individual, relational, and systemic influences. Reducing coercion requires moving beyond individual-level factors to address structural conditions and policy frameworks. Future research should prioritize qualitative and intersectional approaches and amplify the voices of those most affected by coercive practices in psychiatric care.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".