Client safety in assisted living: perspectives from clients, personal support workers and administrative staff in Toronto, Canada
Bibliographic record
Abstract
As the population ages, the demand for long-term care settings is expected to increase. Assisted living is a suitable and favourable residence for older individuals to receive care services specific to their needs while maintaining their independence and privacy. With the growing transition of older individuals into assisted living, facilities need to ensure that safe care is continually maintained. The purpose of this study was to determine the gaps and strengths in care related to safety in assisted living facilities (ALFs). A qualitative descriptive research design was used to provide a comprehensive understanding of client safety from the perspectives of clients, administrative staff and personal support workers. Interviews were conducted with 22 key informants from three ALFs in Toronto, Ontario throughout July 2012. All interviews were semi-structured, audio-recorded and transcribed verbatim. Initial deductive analysis used directed coding based on a prior literature review, followed by inductive analysis to determine themes. Three themes emerged relating to the safety of clients in ALFs: meaning of safety, a multi-faceted approach to providing safe care and perceived areas of improvement. Sub-themes also emerged including physical safety, multiple factors, working as a team, respecting clients' independence, communication and increased education and available resources. The study findings can contribute to the improvement and development of new processes to maintain and continually ensure safe care in ALFs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".