Employers’ Response to Workers With Progressive Cognitive Impairment: A Systematic Literature Review
Bibliographic record
Abstract
Abstract An aging workforce increases the risk of workers experiencing cognitive decline that may lead to a diagnosis of mild cognitive impairment or early onset dementia (MCI|EOD) while still employed. This systematic review explores the use of technologies (defined as any methods, processes, software, hardware or equipment) deployed by employers to accommodate, or build sustainable workspaces for, workers diagnosed with MCI|EOD. After screening 3,860 titles/abstracts and 67 full text reviews, we identified and analyzed eight articles that met our inclusion criteria. We found that: 1) The existing literature almost exclusively focuses on employees’ perspectives on the quality of work life when diagnosed with MCI|EOD, 2) Negative workspace culture toward employees’ cognitive decline, and the variability of disease onset and progression, may account for low employer awareness, 3) Employer responses focus on mitigation of risk associated with workers’ impairment. While this review demonstrates there is scant research exploring employers’ perspectives on employees diagnosed with MCI|EOD, there is even less that explores technologies designed to specifically address employers’ needs and challenges. Technology will increasingly facilitate early identification of progressive neuro-cognitive disorders, and tools to help employers respond to an employee’s MCI|EOD disclosure as a disability accommodation rather than a terminal performance management challenge. Empathic research, that engages organizations in the process of understanding the value of affordable, employer-side technologies that help build diverse, sustainable, productive workspaces is critical to a foundational understanding of our aging workforce and accommodating workers who develop MCI|EOD while still employed.
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 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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".