Creating technology for and with people with MCI and dementia in the workplace
Bibliographic record
Abstract
Abstract Background The potential of technology to assist people who are diagnosed with MCI or early dementia (EOD) whilst still working is under‐explored. Method Semi‐structured interviews and participatory design sessions were held with people working with MCI/EOD and family care partners aged between 45‐65 years to identify their technology preferences, challenges they faced, redesigning their workspace and future solutions. Sessions were video and audio recorded for analysis of the problems with NVivo 12 and to elicit design considerations. Results Five themes emerged from the interviews and participatory sessions: 1. Challenges at work, 2. Accommodations at work, 3. Diversity of supportive technologies, 4. Pressure points and strategies, and 5. Potential ideas and design cues. Conclusion People working with MCI/EOD currently appropriate technology to support them, have clear ideas of the challenges they face and design suggestions for future technological supports. These should be viewed within the ecosystem surrounding individuals working with MCI/EOD, particularly workplace legislation and stimulus for employers to meet their needs.
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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 | 0.009 | 0.012 |
| 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 | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".