A Focused Ethnography of Researchers Navigating Power Dynamics When Implementing Patient-Oriented Research Practices in a Forensic Mental Health Setting
Bibliographic record
Abstract
This ethnographic study explores how a research team navigated power dynamics while developing patient-oriented research practices in a high-secure forensic mental health care setting. Data were collected through team meetings, interviews, and field notes. The study explored how power was understood and addressed at individual, interpersonal, and structural levels. The three themes and six subthemes focused on the importance of acknowledging power within the system, power given to the project through community support, and power held by researchers. Researchers learned to navigate strict policies, procedures, and practices within the forensic environment, focusing on building trust with staff while ensuring patient autonomy and engagement. Equitable communication, particularly with patients, was critical in garnering support for patient-oriented research, often requiring the use of accessible language. Lastly, reflexivity allowed the research team to critically reflect on their biases and positionalities, fostering power-balanced relationships essential for authentic engagement. Findings suggest that addressing power imbalances early and often, and building on the support of staff champions, are key considerations for conducting patient-oriented research in forensic mental health settings.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".