Characteristics of a successful collaboration in evaluation of a health care innovation: lessons learned from GPS locator technology for dementia clients
Bibliographic record
Abstract
Don Juzwishin,1 Madiha Mueen,2 Antonio Miguel Cruz,3,4 Tracy Ruptash,5 Shannon Barnard,6 Meghan Sebastianski,1 Rosmin Esmail,7 Lili Liu4 1Health Technology Assessment and Innovation, Alberta Health Services, 2Department of Family Medicine, University of Alberta, Edmonton, AB, Canada; 3School of Medicine and Health Sciences, Universidad del Rosario, Bogotá D.C., Colombia; 4Department of Occupational Therapy, Faculty of Rehabilitation Medicine, Edmonton, 5Continuing Care Special Initiatives, Seniors Health, Community, Seniors, Addiction and Mental Health, Alberta Health Services, Grande Prairie, AB, 6Integrated Home Care, Alberta Health Services, 7Health Technology Assessment and Adoption, Alberta Health Services, Calgary, AB, Canada Abstract: Becoming lost or its risk is a problem for dementia clients, their families and caregivers. The purpose of the paper is to describe, analyze and share lessons from a pilot project to use global positioning system devices to manage the risk of becoming lost and, at the same time, maintaining client autonomy. The study informs technology implementation approaches and strategies for innovative health technologies. The project used a prospective mixed-methods approach including a pre and post paper-based questionnaire, focus groups and individual interviews. Technology uptake was examined post knowledge transfer using the After Action Review method, which has shown utility in military and health care settings. Project successes and weaknesses are identified to inform future approaches of innovative health technology pilot projects. Lessons from the pilot emphasize the need for innovators to understand the multifaceted context they are entering, enlist the support of leaders, dedicate a project lead, support autonomous decision making and problem solving, meet regularly to monitor progress and address issues and support peer-to-peer collaboration. Keywords: evaluation, innovation, GPS, technology, adoption
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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 | 0.025 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".