Coconstructing a Flexible At-Home Respite Model For and With Caregivers of Older Adults: A Living Lab Approach
Bibliographic record
Abstract
At-home respite services seem too rigid to meet the needs of older adults and their caregivers. It is critical to develop service flexibility, as it allows for personalized care, adapted to health conditions, preferences, and evolving needs. While no prior studies used coconstruction methods to increase flexibility, this study could offer a better understanding of flexible respite for stakeholders. Using a living lab approach, this article aimed to (1) empirically determine the characteristics of this type of respite model and (2) document the levers and obstacles to consider for its implementation. Starting from a pre-existing flexible respite model named ANAAIS, the research team led workshops and interviews. First, the team carried out 2 workshops (TRIAGE and persona-scenario) with a total of 3 caregivers and 8 homecare professionals or managers. Second, a team member conducted interviews with 3 caregivers and 6 homecare professionals or managers. Content analysis was used on the data. The stakeholders coconstructed a Québec version of ANAAIS, a web application allowing caregivers to request an affordable respite, at the time wanted, and offered by a qualified care worker, through a simple application. Levers and barriers to its deployment are linked to the model’s characteristics as well as its internal and external context. For example, relations and connexions were perceived as a lever to deployment, while the lack of resources was considered an obstacle. This living lab project showed the feasibility and pragmatism of coconstructing a flexible and applicable respite service model.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".