The Influence of Service Factors on Spousal Caregivers' Perceptions of Community Services
Bibliographic record
Abstract
The literature clearly denotes that spouses differ from other family members in their reactions to caregiving, their patterns of service use and their assessment of specific services. Yet, despite their prevalence as caregivers, little is known about their unique perceptions of community services and the factors that impact their experiences with the service system. The purpose of this study was to explore the relative influence of (a) spouses' personal factors (e.g., gender, family support) and (b) service factors (e.g., one-on-one professional support), on spousal caregivers' perceptions of community services. The study employed a survey design with a sample of 73 spousal caregivers caring for their partners with dementia at home. This study found that spousal caregivers have more negative perceptions of the service system when their in-home workers are not informed about their spouses' likes, dislikes and routines. This service factor was the most significant predictor of caregivers' service perceptions. The study further found that most spousal caregivers receive fewer than five consultations from a non-medical professional over a one year period. While spouses longed for more professional support, this service factor was not uniquely associated with service related stress. The policy and practice implications of these findings are 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".