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Record W7036853372

Characteristics of a successful collaboration in evaluation of a health care innovation: lessons learned from GPS locator technology for dementia clients

2017· other· en· W7036853372 on OpenAlexaboutno aff

Bibliographic record

VenueDove Medical Press (Taylor and Francis Group) · 2017
Typeother
Languageen
FieldSocial Sciences
TopicEducation and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Health careStrengths and weaknessesMental healthDementiaAction (physics)Focus groupHealth technology
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.114
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.085
GPT teacher head0.425
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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