The impact of autonomy at work on dementia family carers’ ability to manage care-related emergencies, and use technology to that end: semi-structured interviews in Scotland
Bibliographic record
Abstract
Most people with dementia (PwD) are cared for by unpaid family carers, many of whom must balance caring with paid work. This regularly entails dealing with care-related emergencies (CRE). This study aims to explore the impact of carers’ autonomy at work regarding breaks, schedule, and place on their ability to manage CRE, and use technology to that end. We conducted interviews with 16 working carers of PwD in Scotland. Data were analysed thematically to identify key themes. Autonomy at work appeared on a spectrum from no to complete autonomy. Carers’ position on this spectrum was often dynamic and determined by the nature of their work, their workplace culture and regulations, and their line managers' support – or clients in the case of self-employed carers. Break autonomy allowed carers to use technology to be notified of and delegate the CRE response. Schedule autonomy allowed for an in-person response to CRE. Place autonomy allowed carers to work and care simultaneously, which enabled them to manage CRE immediately but presented them with additional challenges. Distance between workplace and PwD's residence impacted carers’ ability to manage CRE, despite having complete autonomy. Implications for healthcare professionals, service providers, employers, policymakers, and technology developers are presented.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".