Campus to Community Partnerships: Advancing the Field of Design in Long-Term Care
Bibliographic record
Abstract
Long-term Care (LTC) (a.k.a nursing homes, personal care homes, residential care facilities, lodges, assisted living facilities and supportive housing), is both a service and home providing health and personal care primarily to older adults who require support that can’t be met in the community due to challenges related to disability or health. The service is provided by a variety of workers (e.g., clinical, caregiving, administrative, housekeeping, food preparation, facilities management, and activity/ recreation staff) as well as volunteers. Long-term care’s intersection with the field of design is vast, including the design of homes, services, products, workplace technologies, assistive technologies, communication, and information. Design matters in this context. Despite this, design-related research has been largely absent in serving the sector compared to other healthcare domains, which our research team first discovered in examining the role of design in end-of-life and palliative care. To address this gap, students from Carleton University have been working in LTC design with Ontario’s Centres for Learning, Research and Innovation in Long-term Care (CLRI) at Bruyère and various LTC homes in Ontario. This collaboration has become a significant part of a larger research program, the Design for Public Health (D4pH) Lab, where students examine how ‘research for, into and through design’ informs the design of LTC systems, services, products, technologies, and homes. In this paper we profile the work of 4 graduate students to illustrate the integrated, systems design approach they took to study pressing issues in LTC. Students investigated the experiences of multi-site LTC workers during the pandemic and communicating the findings through creative mediums; spiritual care through service design methods; service design for individuals transitioning to LTC and advanced care planning; and a user experience study focused on understanding continence care at a systems level to support dignity and safety in this important care element.
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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 | 0.068 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".