Assessment of the impact of MoCA-Ina (Montreal Cognitive Assessment Indonesia version) scores on depression, burden, and knowledge in dementia caregivers
Bibliographic record
Abstract
Background: Dementia patients necessitate intensive care, often imposing a substantial burden on caregivers. With the rising global prevalence of dementia, it becomes crucial to elucidate the determinants that influence caregiver stress and overall well-being. Objective: This research mainly aimed to examine the impact of the Montreal Cognitive Assessment Ver. Indonesia (MoCA-Ina) score on the stress scale, life burden, and knowledge level among caregivers of dementia patients. Method: Observational research was conducted at Dr Sardjito Hospital Yogyakarta's Memory Clinic between March and May 2023. The study involved dementia patients who met specific criteria. Caregivers completed questionnaires, including the Hamilton Depression Rating Scale (HDRS), Zarit Burden Interview (ZBI), and Dementia Knowledge Assessment Scale (DKAS). The data was analysed using the Spearman correlation test. Result: The research findings from 47 participants showed that the majority of caregivers were male (55.3%), university-educated (38.3%), family members (42.5%), spent over six hours per day with the patient (63.8%), and cared for the patient for varying durations. Caregiver stress scale scores indicated mild depression, mild-moderate burden, good knowledge level, and severe cognitive impairment. The Spearman test found no significant correlation between the cognitive impairment score and caregiver stress, burden, or knowledge level. Conclusion: MoCA-INA score in patients with dementia did not affect the caregiver's stress scale, caregiver burden, or knowledge levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".