Help-seeking for 'memory loss' by older adults in India : patient, caregiver and health providers' perspectives
Bibliographic record
Abstract
ABSTRACT: Background. In India, prevalence rates of dementia and prodromal amnestic Mild Cognitive Impairment (MCI) are 3.1% and 4.3% respectively. Most Indians refer to the full spectrum of cognitive disorders simply as ‘memory loss.’ Barring prevention or cure, these conditions will rise rapidly with population aging. Evidence-based policies and practices can improve the lives of affected individuals and their caregivers, but will require timely and sustained uptake. Objectives. Framed by social cognitive theories of health behavior, this study explores the knowledge, attitudes and practices concerning cognitive impairment and related service use by older adults who screen positive for MCI, their primary caregivers, and health providers. Methods. I used the Montreal Cognitive Assessment to screen for cognitive impairment in memory camps in Mumbai. To achieve sampling diversity, I used maximum variation sampling. Ten adults aged 60+ who had no significant functional impairment but screened positive for MCI and their caregivers participated in separate focus groups. Four other such dyads and six doctors/ traditional healers completed in-depth interviews. Data were translated from Hindi or Marathi to English and analyzed in Atlas.ti using Framework Analysis. Findings. Knowledge and awareness of cognitive impairment and available resources were very low. Physicians attributed the condition to disease-induced pathology while lay persons blamed brain malfunction due to normal aging. Main attitudes were that this condition is not a disease, is not serious and/or is not treatable, and that it evokes stigma toward and among impaired persons, their families and providers. Low knowledge and poor attitudes impeded help-seeking. Conclusions. Cognitive disorders of aging will take a heavy toll on private lives and public resources in developing countries. Early detection, accurate diagnosis, systematic monitoring and quality care are needed to compress the period of morbidity and promote quality of life. Key stakeholders provide essential insights into how scientific and indigenous knowledge and sociocultural attitudes affect use and provision of resources.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".