USING KNOWLEDGE MOBILIZATION TO ADVANCE THE CREATION OF HOMELIKE RESIDENTIAL LONG-TERM CARE
Bibliographic record
Abstract
The development of homelike environments in residential long-term care (LTC) settings can lead to positive health and well-being outcomes for residents. Creation of ‘home’ in LTC requires input from residents and their caregivers. Following the completion of a two-year evaluation project that examined experiences of residents, their family members, and care staff in a LTC facility who had transitioned from an institutional to a homelike setting, practice implications and guidelines were presented during a Research Day. This Research Day was a unique methodological approach informed by iterative knowledge mobilization processes involving interactive data-presentation and data-validation stations. Participants included research participants, local community members, and decision-makers from government sectors and the regional health authority. This presentation provides an overview of findings from this innovative methodological approach and suggests implications for using effective knowledge mobilization strategies to collaboratively advance residential care practice and policy with research participants and professional and community stakeholders.
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 | 0.065 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".