A foundational analysis of internal consistency for 17 geriatric/gerontological outcome measures in Nigerian older adults: Do we need further psychometric evaluation?
Bibliographic record
Abstract
Background and objectives Many gerontological outcome measures developed and validated in advanced countries may produce unreliable results in Nigeria, necessitating psychometric evaluation of these tools. This study provides the foundational psychometric analysis of internal consistency across 17 common gerontological measures among community-dwelling older adults in Nigeria. Research design and methods This is a secondary analysis of 409 (83.9 % females) Nigerian older adults (mean age 67.96±6.67 years) who responded to 17 measures evaluating cognitive, environmental, physical, psychological, and social outcomes. Cronbach’s alpha was interpreted as ≥0.7 (high), close to 0.6 (moderate) and <0.5 (low). Item-test and item-rest correlations were considered acceptable if greater than 0.5 and >0.3, respectively. Results Mini-Mental State Examination, Memory Assessment Clinic Questionnaire, Attention Assessment Scale, Neighbourhood Satisfaction Subscale of the Neighbourhood Environment Walkability Scale (NEWS), Life-space Mobility Questionnaire, 9-Item Modified Physical Literacy Instrument, Geriatric Anxiety Scale, Geriatric Depression Scale, Fall Efficacy Scale- International, Keele Assessment of Participation, University of California, Los Angeles Loneliness Scale, Lubben Social Network Scale, Multidimensional Scale of Perceived Social Support, Connectedness subscale of Social Isolation Scale (SIS) demonstrated high internal consistency (≥0.7). Similarly, Montreal Cognitive Assessment (MoCA) and the belongingness subscale of SIS demonstrated moderate internal consistency (close to 0.6), while other NEWS subscales, Physical Activity Scale for the Elderly (PASE), and the International Personality Item Pool (IPIP) had low internal consistency (<0.5). Conclusion Fourteen measures demonstrated strong internal consistency in this sample, providing preliminary support for their potential suitability in Nigerian older adult population. A complete validation of reliability will require subsequent analyses of dimensionality, test–retest consistency, and measurement invariance across relevant populations to finalise confident use.
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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 | 0.072 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".