Routinized violence: examining long-term residential care workers’ perspectives on involuntary treatment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In long-term residential care (LTRC), sometimes workers provide treatment that residents refuse or resist, which can cause harm to both workers and residents. In this analysis, we explored how and when workers provide involuntary treatment, when they accept or see this practice as necessary and when they reject this practice. RESEARCH DESIGN AND METHODS: Following a qualitative research design, data were collected through interviews with nurses, health care aides, recreation, and housekeeping staff in two Canadian provinces and observations in two LTRC facilities in the province of Manitoba. Using an interpretive coding approach and guided by Foucauldian concepts of power and structural violence, we examined descriptions of violent situations and everyday interactions with a particular focus on involuntary treatment. RESULTS: Beliefs about the potential for physical harm toward workers influenced the perceived acceptability, or rejection, of involuntary treatment. However, workers often expressed ambivalence about the acceptability of certain practices (e.g., using multiple workers to hold down a resident to provide personal care). The potential for worker injury and risk of being reprimanded were frequently identified by workers as shaping their decisions about whether to proceed with treatment to which the resident had not consented. At times, workers also expressed obligation to provide involuntary treatment for biomedical reasons, or because there seemed to be no good alternative. DISCUSSION AND IMPLICATIONS: Workers' narratives about involuntary treatment reflect a lack of interpersonal and organizational safety that undermines the autonomy and dignity of those for whom they provide 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| 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".