Deciding on Institutionalization for a Relative with Dementia: The Most Difficult Decision for Caregivers
Bibliographic record
Abstract
The decision to move a family member with dementia to a nursing home is a difficult experience for caregivers. Complex psychosocial factors are involved and knowledge of predictive factors alone is insufficient. Using grounded theory, this study explores the decision-making process with regards to institutionalization, from the perspective of family caregivers. Fourteen people who moved a relative to long-term care in the preceding 6 months were interviewed. Data analysis using comparative analysis and line-by-line dimensional analysis was used to develop a theoretical model of the decision-making process. Three factors within the model were central to the process: (a) caregivers' perceptions of their ability to provide care, (b) caregivers' evaluations of their relatives' ability to make care decisions, and (c) the evolving influence of contextual factors and interactions with healthcare professionals. The contribution of these findings to new conceptualizations of institutionalization is discussed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".