Caregiver Enablement Training to Support Homecare: A Study from India
Bibliographic record
Abstract
Abstract Homecare is the prevalent model of dementia care in India and ‘attendants,’ who are non‐trained lay workers are often employed in Indian households to assist with personal care, ADL and other support which the family may need. Providing training to these attendants can support continued dementia care at home. We present initial data from a caregiver enablement program (CEP) being provided in India. Methods The (CEP) consists of a minimum of 6‐8 individually tailored sessions beginning with a clinical evaluation. Using a collaborative approach, challenging areas of caregiving are identified and prioritized. Across the sessions, information about what may be contributing to these challenges and tips for providing care are provided by a dementia trained specialist. Attendants and family members are encouraged to use these approaches and feedback was discussed in an iterative manner to improve the caregiving process and address emerging issues. Results Ten families availed the caregiver enablement program, out of those 2 patients were living with their 24 hours attendant and 8 patients were living with family and attendants. The caregiver enablement program was conducted online, offline and in hybrid mode, 5 of them were online sessions, 4 offline sessions and one hybrid session (session conducted at home and online). Patients had moderate to severe stage dementia (Hindi MMSE Mn = 13.3, SD = 5.7; Caregiver burden: Zarit burden interview Mn = 29.8 SD = 19.6). Key areas where enablement was required were basic understanding of dementia, acceptance of the illness by the family and that they need external support, how to manage challenging behaviours like toileting, bathing, dressing, incontinence, transferring and feeding, how to engage the patient in meaningful activities and the need for environment modifications to improve safety and managing challenging behaviours. Psycho educational skill building, multi component intervention was found to be effective in the present study to combat the concerns shared by the attendants/family members. Conclusion This study demonstrates how up‐skilling lay workers through training on dementia and management of challenging behaviours can support home care. Supporting the informal care system with specialised training can be an effective way of providing dementia care in low resource settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".