An exploratory study of caregiver burden amongst urban upper class adults in India during COVID‐19
Bibliographic record
Abstract
Abstract Background The COVID pandemic has impacted caregivers of older adults globally and families have been wary of approaching hospitals due to increased risk of exposure. We explored reasons for help seeking and caregiver burden amongst a sample of consecutive urban upper class adults who approached a non‐hospital based organization providing elderly support in Delhi, India. Methods A detailed assessment was carried out by trained care specialists using a clinical interview, Hindi Mental Status examination, evaluation of independence in activities of daily living and caregiver burden using the Zarit Burden Interview (ZBI). The assessment was conducted online or in person depending on comfort of the family. Descriptive analysis was carried out to understand the caregiver profile and reasons for seeking help. Results Of the 39 adults assessed, majority had a primary diagnosis of dementia (80%). 10% had other psychiatric illness and 10% other neurological illness. 87% families preferred an in person assessment, stating that an online assessment would not be possible with their family member. Primary caregivers were mostly female (80%) and older adults (46%). The main reason for seeking help was inability to manage challenging behaviors at home (97%), followed by concerns about increased cognitive impairments during COIVD (80%), mood related disturbances (41%) and difficulties with activities of daily living (41%). Assessment using ZBI revealed 82% primary caregivers experienced mild to moderate caregiver burden, however, they felt that they were doing their duty and none spontaneously reported a need to seek any form of support for themselves. Conclusion Currently India does not have a public health system that supports dementia care and public awareness about management is limited. Our findings highlight that caregivers experience significant stress particularly managing challenging behaviors. Most caregivers are female older adults in whom caregiver burden may be more due to factors like role‐strain and limited independence. In absence of a supportive public health system, our findings also highlight the important role that non‐hospital based organizations play in bridging a gap for dementia care delivery, particularly during these COIVD times.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".