BUILDING COMMUNITY IN PRACTICE AND RESEARCH OF HEALTHY LONG-TERM CARE ENVIRONMENTS
Bibliographic record
Abstract
Abstract This presentation will describe an initiative based at the Institute of Health Policy, Management and Evaluation in Toronto, Canada, that aims to dismantle silos and build a community in the practice and research of aging environments. The goals and objectives are to share, synthesize, advance, and mobilize knowledge on long-term care homes—the influence of living environments on older adults and working environments on workers and their work. Three knowledge mobilization activities are the means by which our aims are achieved: 1) A series of seminars are curated for experts to disseminate knowledge to a broad audience (industry and academic leaders, decision-makers, and end-users) to connect research to practice. 2) Informal, practical discussions are created for practitioners to share current key challenges in long-term care homes with researchers to connect practice to research. 3) A Design Charrette is organized to translate knowledge to address key challenges within interdisciplinary teams comprising practitioners/ care providers, team leads, and trainees. These activities bring together individuals from industry and academia who represent diverse disciplines that have generated knowledge about these linkages based on different and complementary conceptualizations of long-term care environments but for whom there is no pre-existing forum in which to engage in integrative, synthetic discussion and application. The open-forum nature of the activities is intended to foment public discourse on the role of physical and psychosocial environments in the health of older adults and workers in long-term care homes and to demonstrate the value of evidence-based design founded on multidisciplinary knowledge.
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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 | 0.214 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.027 | 0.044 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.009 | 0.069 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".