Abuse in Canadian long-term care homes: a mixed methods study
Bibliographic record
Abstract
OBJECTIVE: To examine reported cases of abuse in long-term care (LTC) homes in the province of Ontario, Canada, to determine the extent and nature of abuse experienced by residents between 2019 and 2022. DESIGN: A qualitative mixed methods study was conducted using document analysis and descriptive statistics. Three data sources were analysed: LTC legislation, inspection reports from a publicly available provincial government administrative database and articles published by major Canadian newspapers. A data extraction tool was developed that included variables such as the date of inspection, the type of inspection, findings and the section of legislation cited. Descriptive analyses, including counts and percentages, were calculated to identify the number of incidents and the type of abuse reported. RESULTS: According to legislation, LTC homes are required to protect residents from physical, sexual, emotional, verbal or financial abuse. The review of legislation revealed that inspectors are responsible for ensuring homes comply with this requirement. An analysis of their reports identified that 9% (781) of overall inspections included findings of abuse. Physical abuse was the most common type (37%). Differences between the frequency of abuse across type of ownership, location and size of the home were found. There were 385 LTC homes with at least one reported case of abuse, and 55% of these homes had repeated incidents. The analysis of newspaper articles corroborated the findings of abuse in the inspection reports and provided resident and family perspectives. CONCLUSIONS: There are substantial differences between legislation intended to protect LTC residents from abuse and the abuse occurring in LTC homes. Strategies such as establishing a climate of trust, investing in staff and leadership, providing standardised education and training and implementing a quality and safety framework could improve the care and well-being of LTC residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".