Exploring perspectives of young onset dementia caregivers with high versus low unmet needs
Bibliographic record
Abstract
BACKGROUND: This study is part of the Research to Assess Policies and Strategies for Dementia in the Young project. Information about specific needs in young onset dementia (YOD) will provide the basis for the development of an e-health intervention to assist caregivers in coping with YOD in several European countries. OBJECTIVE: The aim was to investigate the issues caregivers of people with YOD face. METHODS: A qualitative content analysis method was used to analyse interviews with YOD caregivers. Quantitative data of the Needs in Young Onset Dementia study were used to select caregivers based on a ranking of unmet needs, to capture differences and similarities between caregivers that experienced high levels of unmet needs versus those with low levels of unmet needs. Needs were assessed with the Camberwell Assessment of Needs in the Elderly. RESULTS: Findings revealed the following themes: (i) acceptance; (ii) perception of the relationship; (iii) role adaptation; (iv) Availability of appropriate services; (v) social support; and (vi) awareness in the person with dementia and acceptance of help. Several factors seemed more apparent in the caregivers who experienced few unmet needs opposed to the caregivers who experienced more unmet needs. CONCLUSION: The current study provides an in-depth perspective on the caregiver's experiences and emphasizes specific themes that could be addressed in future interventions. This might contribute to a caring situation in which the caregiver experiences less unmet needs. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".