Impact of the Relocation of a Long-term Residential Care Facility on Staff
Bibliographic record
Abstract
This article describes the relocation of residents and staff of a long-term residential care facility into a new state-of-the-art building in a Canadian province. All staff were surveyed about their perceptions of the moving process 2 months after the move occurred using a newly created 51-item questionnaire containing both open-ended and closed questions (5-point Likert scale). The results were positive for the 3 subscales of the survey, with average scores for premove, midmove, and postmove items of 3.67, 3.94, and 3.66, respectively. There was no significant difference in the means when comparing staff position, years of employment, or assignment to 1 or more units. Staff were very positive about the move itself, the orientation provided and overall planning, and support from coworkers and management. Some concerns were raised about staffing shortages, involvement of residents, and preparedness of the units and building. In addition, it is evident that relocation is an ongoing process, with many supports required in the months after the move. This article describes a very well planned and executed relocation of a long-term residential care facility and can provide guidance and lessons learned to assist other administrators who are planning a similar endeavor.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".