Working towards inclusion: Creating technology for and with people living with mild cognitive impairment or dementia who are employed
Bibliographic record
Abstract
Earlier diagnosis and longer working careers is resulting in more individuals being identified as having Mild Cognitive Impairment or Early Onset Dementia (MCI/EOD) when they are still in the workforce. While there is growing interest in the dementia research community and beyond to develop technologies to support people with dementia, the use of technology for and by people with MCI/EOD in the workplace has had very little attention. This paper presents a two-part study involving interviews and participatory sessions to begin to understand the workplace experiences and the role of technology among people living with MCI/EOD. We present our findings from working with seven people with MCI/EOD and two care partners to explore technology design. Our results indicate several similarities as well as a few differences between MCI/EOD and later-onset dementia with respect to challenges using technology and design considerations for supporting engagement and use of technology. Lessons learned through the process of working with people with MCI/EOD through participatory methods is presented along with recommendations to foster an inclusive, respectful, and empowering experience for participants with MCI/EOD.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| 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".