Depression Level and Burden of Care Among Family Caregivers of Older People With Physical and Mental Disability in Makkah City KSA
Bibliographic record
Abstract
AIMS: This study is aimed at examining the burden of care and depression level among the family caregivers of client diagnosed with physical or mental disability. In addition, this study purposed to test the relationship between socio-demographic factors and level of depression and burden of care. METHODS: Study design was a descriptive survey design. Study sample was 129 family caregivers of patients diagnosed with physical and mental disabilities in Makkah, Kingdom of Saudi Arabia. Data collected using a pre-designed structured interviewing questionnaire including the Beck Depression Inventory scale (Beck, Steer, & Brown, 1996) and Family Burden Interview Schedule. RESULTS: Percentage of depression level ranged from 63% among caregivers of physically disorder clients to 69% among caregivers of mentally disorder clients. Moreover, there were many factors that may increase risk of depression such as old age of caregivers, spouse and caregivers who cared for their charges four hours or more per day. There was significant difference in depression level and burden of care as regard to nature of relative illness (p <0.05). CONCLUSIONS: Policies and programs to alleviate the burden of care and to provide social support for these family caregivers are equally important for both family caregivers and their care receivers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".