Modeling the Distress of Spousal Caregivers of People with Dementia
Bibliographic record
Abstract
BACKGROUND: The progressive mobilization of spouse caregivers who take care of a person with dementia (PWD) can lead to situations of distress. OBJECTIVE: The current study sought to investigate the influence of the characteristics of the caregiving context on spousal caregiver distress. METHODS: 125 spousal caregivers participated in this study. The characteristics of the caregiving context were assessed using questionnaires. We examined a moderated-mediator model (Step 1) in which we hypothesized that PWD and caregiver characteristics and dyadic determinants contribute to spousal caregiver distress. This model was compared based on the age at onset of the disease and the gender of the caregiver (Step 2). RESULTS: The model revealed that poor self-rated health and a lack of family support accentuated spousal caregiver distress, whereas the feeling of being prepared and level of confidence decreased spousal caregiver distress. Moreover, the quality of couple adjustment affected spousal caregiver distress, and this effect was mediated by the severity of the PWD's symptoms. Regarding the age at onset of the disease, the path between Couple Adjustment and the Care recipient's impairments was more important for caregivers of person with early-onset dementia (PEOD). Female caregivers who reported poor self-rated health experienced greater distress. CONCLUSIONS: It would be interesting to create a support program that would incorporate these three areas of intervention regarding the progression of the disease: first, "preparedness modules"; second, "dyadic modules" (especially for caregivers of PEOD); and third, "family modules". Specific attention should be given to female caregivers who report poor self-rated health.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".