Cross-cultural adaptation of assessments for time-related abilities of Indian older adults and evaluation of their reliability and validity
Bibliographic record
Abstract
Background Older adults may have difficulties in daily time management due to age-related or disease-related cognitive impairment. Standardised assessments for time-related abilities are currently unavailable in India.Aim The study aimed to adapt the Kit for Assessing Time-processing Ability–Senior (KaTid-Senior) and Time-Self rating, Senior (Time-S Senior) for daily time management of Indian older adults, translate these into an Indian language, and evaluate the reliability and validity of the adapted assessments.Materials and methods The two Swedish-origin assessments were reviewed, adapted for linguistic and cultural relevance into English, and translated into Kannada language. Older adults (n = 128) were conveniently selected, assessed with the Montreal Cognitive Assessment, and assigned to age and gender-matched groups: cognitively-impaired and cognitively-normal. Data was then collected with the adapted assessments.Results Both adapted assessments demonstrated acceptable reliability (internal consistency) in this sample (α =0.89 − 0.90). The cognitively-impaired group had significantly (p < 0.001) lower scores on the assessments as compared to the cognitively-normal group. There was a strong to moderate correlation between the assessments supporting their convergent validity.Conclusions The adapted assessments are reliable and valid in the Indian context.Significance The study would facilitate contextually-relevant assessment and management of time-related abilities in Indian older adults.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".