The legal implications of dementia in the workplace: establishing a cross-disciplinary research agenda
Bibliographic record
Abstract
ABSTRACT Dementia is a growing issue in the United Kingdom (UK) with over 800,000 people affected. Of these people, in excess of 40,000 are aged under 65 years. Thus, a significant number of individuals may be experiencing symptoms of dementia while in employment. In addition, as working lives extend, the potential impact of dementia on the workplace could be substantial. However, to date, there has been little research on experiences of dementia in the workplace. The research that exists highlights the lack of support for workers with dementia. Dementia may be considered to be a disability under the Equality Act 2010. Therefore, the legislation potentially provides a framework for individuals to request that their employer make reasonable adjustments to support their continued employment. International human rights law is potentially another tool that could be utilised to obtain necessary adjustments. This paper argues that in developing the evidence base on workplace experiences of dementia, it is important that the legal framework be considered. This paper reviews the existing literature on dementia in the workplace and embeds this in the legislative framework in order to establish a cross-disciplinary research agenda. While the paper focuses on the UK legal context, the argument presented in the paper is still relevant to other national contexts.
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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 | 0.115 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.018 | 0.045 |
| Scholarly communication | 0.029 | 0.042 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".