Use of a Persona to Support the Interdisciplinary Design of an Assistive Technology for Meal Preparation in Traumatic Brain Injury
Bibliographic record
Abstract
Abstract User-centered design (UCD) facilitates the creation of technologies that are specifically designed to answer users’ needs. This paper presents the first step of a UCD using a persona, a fictitious character representing the targeted population, which in this case is people having sustained a traumatic brain injury (TBI). The persona is used to better understand the possible interactions of a TBI population with a prototype of a technology that we wish to develop, namely the Cognitive Orthosis for coOKing (COOK). COOK is meant to be an assistive technology that will be designed to promote independence for cooking within a supported-living residence. More specifically, this paper presents the persona’s creation methodology based on the first four phases of the persona’s lifecycle. It also describes how the persona methodology served as a facilitator to initiate an interdisciplinary collaboration between a clinical team and a computer science team. Creation of personas relied on a clinical model (Disability Creation Process) that contextualized the needs of this population and an evaluation tool [Instrumental Activities of Daily Living (IADL) Profile] that presented a wide range of cognitive assistance needs found in this same population. This paper provides an in-depth description of some of the most frequent everyday difficulties experienced by individuals with TBI as well as the persona’s abilities, limitations and social participation during the realization of IADL, and an evaluation of the manifestations of these difficulties during IADL performance as represented through scenarios. The interdisciplinary team used the persona to complete a first description of the interactions of a persona with TBI with COOK. This work is an attempt at offering a communication tool, the persona, to facilitate interdisciplinary research among diverse disciplines who wish to learn to develop a common language, models and methodologies at the beginning of the design process.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".