“It’s for their own good”: Techniques of neutralization and security guard violence against psychiatric patients
Bibliographic record
Abstract
Based on eight in-depth interviews conducted with security guards who work in the psychiatric units of two hospitals located in Ottawa, Canada, this research found that private security agents unknowingly draw on Sykes and Matza’s five techniques of neutralization to justify their use of violence and coercive force against patients and to overcome their feelings of guilt for participating in the administration of brutal intervention practices, including physical and chemical restraints. Guards claimed that these practices benefit the patients more than it hurts them and in cases where they believed the interventions to be unwarranted, guards either accepted the medical staff’s judgment to make decisions about when coercive force should be used or condemned the authority the nursing staff has in determining how to manage patients. They also drew on militant codes of security conduct to justify their tough demeanours and resilient attitudes towards medicalized violence. Implications for forensic practice include considerations of the effects of the (gendered) power relations that structure closed institutional settings and can harm already vulnerable patients, as well as the negative consequences of using security to enforce arbitrary institutional rules.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| 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".