Moving forward with dementia: an explorative cross-country qualitative study into post-diagnostic experiences
Bibliographic record
Abstract
OBJECTIVES: This explorative cross-country qualitative study aims to describe experiences of receiving a dementia diagnosis and experiences of support following a diagnosis in Australia, Canada, the Netherlands and Poland. METHOD: Qualitative study using projective techniques during online focus groups, online and telephone interviews with people with dementia and caregivers. RESULTS: Twenty-three people with dementia and 53 caregivers participated. Qualitative content analysis revealed five themes; (1) 'Coming to terms with dementia' helped people deal with complex emotions to move forward. (3) 'The social network as a source of support' and (4) 'The challenges and realities of formal support' and impacted 'Coming to terms with dementia'. (2) 'Navigating life with dementia as a caregiver' highlights caregiver burden and was impacted by (4) 'The challenges and realities of formal support'. People were (5) 'Self-caring and preparing for tomorrow' as they focused on maintaining current health whilst planning the future. Despite differences in healthcare and post-diagnostic support systems, there were more similarities across countries than differences. CONCLUSION: Across countries, formal support and support from friends and family are crucial for people with dementia and caregivers to come to terms with dementia and maintain carer wellbeing to ultimately live well with dementia.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".