What happens when people develop dementia whilst working? An exploratory multiple case study
Bibliographic record
Abstract
PURPOSE: This study is an in-depth exploration of the unfolding experiences of five persons who developed dementia while still in paid work/employment, and of their significant others. Namely, we explore how they experienced the actions and decisions taken with respect to work, and what the consequences meant to them. METHODS: A qualitative longitudinal case study design with multiple cases was used, including five participants with dementia and significant others of their choice. Interviews were undertaken longitudinally and analysed with the Formal Data-Structure Analysis approach. RESULTS: The joint analysis resulted in two intertwined themes: 1) The significance and consequences of a dementia diagnosis: a double-edged trigger, and 2) Sensemaking and agency. The prevalent images of what dementia is, who can/cannot get it and what it will bring, were revealed as the critical aspects. Having the opportunity to make sense of what has happened and participate in decision-making, contributed decisively to the participants' experiences. CONCLUSIONS: Findings illustrate how a dementia diagnosis is alien in work-life, but once diagnosed, it may trigger self-fulfiling expectations based upon stereotypical understanding of dementia. A shift is needed from a deficit-focused perspective, to viewing people with dementia as citizens capable of agency.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".