TRANSITIONS EXPERIENCED BY PEOPLE DIAGNOSED WITH DEMENTIA WHILE IN THE WORKFORCE
Bibliographic record
Abstract
Abstract Dementia is a progressive, irreversible neurological disorder that causes changes in cognitive function and behaviour. While at least 5% of people who develop dementia every year are under the age of 65, dementia in the workplace is currently not well recognised or supported. The changes associated with dementia present multiple challenges for individuals who wish to continue with their employment. Many lose their positions before receiving a diagnosis, whilst others take sick or disability leave or early retirement. The process of understanding what is happening and coping with this new situation is highly individualistic and involves several transitions. The MCI@work project is an international initiative taking place in Canada, Finland, and Sweden where we examine these transitions through the personal narratives of individuals who have either currently or recently gone through the experience of developing dementia whilst in the workforce. From these data, we have developed a framework for understanding the transitions experienced by people who develop dementia whilst in the workforce. The aim is to assist individuals and their employers to better understand the needs of people living and working dementia as well as engage in appropriate actions that support choices and dignified transitions either within the context of employment or out of the workforce.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".