Psychometric properties of the Cognitive Functioning Self-Assessment Scale in community-dwelling adults: A cross-sectional online survey
Bibliographic record
Abstract
Background The Cognitive Functioning Self-Assessment Scale (CFSS) was developed to allow the self-assessment of perceived cognitive functioning. It has been tested with satisfactory reliability and validity but was not available for the Chinese population. This study aimed to adapt and validate the Chinese version of the CFSS for community-dwelling adults. Methods A cross-sectional study of a sample of 1,002 Chinese adults from the general population was conducted online (July–August 2022). The Chinese version of the CFSS with 18 items was created through translation, cognitive debriefing, and psychometric evaluation. Exploratory factor analysis (EFA) was performed on the first half of the randomly split sample. A model derived from EFA was confirmed by confirmatory factor analysis (CFA) in the second half of the sample. The model fits were further evaluated with and without subgrouping by age, gender, and education level. Internal consistency was assessed using Cronbach’s alpha (α) and McDonald’s omega hierarchical coefficients (ωH). Results EFA (n = 460) revealed a three-factor solution, including spatio-temporal orientation, attention, and memory, which explained 51% of the total variance. The second-order CFA (n = 510) demonstrated a good fit: CFI = 0.951, TLI = 0.944, RMSEA = 0.054, SRMR = 0.040. A second-order multiple-group analysis showed that the structure was invariant by age, gender, and education level. The total CFSS score was significantly associated with the Chinese version of the nine-item Patient Health Questionnaire (r = 0.56, p< 0.001) and the Chinese version of the seven-item General Anxiety Disorder (r = 0.53, p<0.001). The internal consistency reliability was satisfactory, with α = 0.94 and ωH = 0.84. The item-total correlation coefficients ranged from 0.58 to 0.72. Conclusion The Chinese version of the CFSS possesses good item characteristics, satisfactory validity, and reliability for assessing self-reported cognitive functioning among community-dwelling adults. It is a feasible and appropriate self-assessment instrument to examine cognitive functioning in the Hong Kong Chinese population, which contributes to monitoring and developing strategies to prevent and manage cognitive impairment and disorders for the public’s cognitive health and well-being.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".