Employers ’ Response to Workers With Progressive Cognitive Impairment: A Review of Policy in Canada
Bibliographic record
Abstract
Abstract Longer lifespans, the gig economy, eligibility for government pensions, and more testing for age-related cognitive changes, increase the potential for workers developing mild cognitive impairment and/or early onset dementia (MCI|EOD) “on the job”. This critical analysis assesses Canada’s policy environment for employers when employees are diagnosed with MCI|EOD. Our search for policy literature included: a scoping review of academic literature involving Canadian-focused articles, and countries where novel or innovative policy had been evaluated and published; a search for Canadian court judgements and tribunal decisions; and a grey literature search in both Canadian and international sources, as innovation will often happen “at the margin” and updated policy may take years to be enacted and formalized. We used participatory research to obtain feedback from a broad group of stakeholders including employers, industry, professional organizations, and government, as well as people living with MCI/dementia, to ensure outputs were reflective of current policy. We found that: 1) Canadian federally-regulated employers are governed by similar Acts & Codes as the provinces and territories, with some notable exceptions, 2) Disability discrimination and accommodation case law in Canada is settled, however there are few cognitive impairment cases to provide specific guidance, 3) Scant empirical research in the scientific literature addresses policy that incents employers to build workspaces for employees with MCI|EOD that help them stay on the job longer. We conclude that engaging with employers to better understand their needs will help policy-makers to support them build workspaces that encourage productive engagement of all workers.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".