A case study of integrated knowledge translation in the context of en masse interinstitutional relocation of a long-term care home in Canada
Bibliographic record
Abstract
En masse interinstitutional relocations for residents can cause distress, increased behavioural issues, increased health concerns, though for some, there are improvements in health and cognitive functioning.Most negative effects of relocation are temporary and can be mitigated by preparation prior to the move and a supported transition period post-move.The most difficult period is shortly before the move and three to six months post-relocation. 2En masse interinstitutional relocations for team members can cause stress related to job security, requirements to learn new operating systems and procedures, establishing new team and working relationships, loss of previous relationships, and the ability to provide care to the same standard as the previous home.Stress that results from such change can lead to burnout, sick leave, and turnover, but can be mitigated by real engagement, consistent and clear communication, and strong management.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".