THE PROBLEM WITH NO SOLUTION? EXPLORING UNWANTED SEXUAL ATTENTION IN RESIDENTIAL LONG-TERM CARE
Bibliographic record
Abstract
Although unwanted sexual attention has been linked with care providers’ negative health and occupational outcomes, limited research has explored this in the context of long-term care homes. The purpose of this ethnographic study was to explore how care providers in one home in Ontario, Canada understood and responded to unwanted sexual attention from residents, and to explore how individual experiences were shaped by organizational and structural factors. Data collected includes participant observation and in-depth interviews with providers (n=25) across multiple professions and roles, as well as a discursive analysis of relevant documents. While participants reported multiple types of sexual attention and that these were uncomfortable or unwanted, they rarely reported these formally as they perceived these as unavoidable aspects of care work. Participants normalized sexual attention as a dementia-related symptom or mental pathology, and did not believe that systemic prevention was possible. As a result, they typically ignored sexual advances, distracted or redirected residents, and focused on completing their tasks. They also reported a lack of formal education and training regarding the management of unwanted sexual attention and expressed the desire for more assistance with managing sexual dynamics in practice. Our analysis suggests that participants’ normalization of unwanted sexual attention is driven by reductive legal mechanisms for prevention of sexual harassment, as well biomedically focused dementia care education and training. Implications of these findings for policy and practice will be discussed.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".