Challenges in the Use of the Montreal Cognitive Assessment (MoCA) in Primary Health Care Settings in India
Bibliographic record
Abstract
Background: With a rapidly ageing population, dementia rates are rising globally, posing challenges to individual health and healthcare systems. Early dementia detection is crucial for effective intervention and improved patient quality of life. The Montreal Cognitive Assessment (MoCA) is a commonly used cognitive screening tool with high sensitivity and specificity. However, implementing MoCA in primary healthcare (PHC) settings in India is met with significant challenges. Methods: This qualitative study explored the barriers healthcare professionals face in using MoCA within PHC settings in Karnataka, India. Twelve healthcare providers, including physicians and nurses, participated in semi-structured interviews. Data were analyzed using thematic analysis, yielding insights into the experiences and perspectives of PHC professionals regarding MoCA use. Results: Five major themes emerged: (1) insufficient training and knowledge about administering MoCA, (2) time constraints in busy clinical environments, (3) cultural and linguistic relevance of the assessment tool, (4) limited resources and accessibility in PHC settings, and (5) lack of integration with other health services. These barriers hindered effective cognitive screening, potentially leading to delayed diagnosis and intervention for cognitive impairment. Innovation: This study uniquely highlights the systemic barriers to implementing MoCA in India's PHC settings and proposes culturally relevant adaptations to the tool. The findings emphasize the importance of integrating cognitive screening within existing healthcare workflows, supported by tailored training programs and resource allocation strategies. These insights provide a foundation for creating context-specific cognitive screening tools and methodologies, advancing early detection of dementia in low-resource settings. Discussion and Conclusions: This study underscores the need for systemic changes to improve MoCA’s usability in PHC, including enhanced training, adaptation of the tool for acultural relevance, improved resources, and integrated care models. Addressing these barriers may enhance the early detection and management of cognitive disorders, fostering a more holistic approach to patient care in India’s PHC 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".