Development of a tool measuring various aspects of social detachment: The social detachment questionnaire for older population
Bibliographic record
Abstract
BACKGROUND: Social detachment includes the subjective aspect "loneliness" and the objective aspect "social isolation," but tools to assess both dimensions are limited. This study aims to develop a questionnaire, the Social Detachment Questionnaire for Older Population (SDQO), that considers multiple dimensions of social detachment simultaneously. METHODS: The study collected 600 valid samples from individuals aged 55 and above to examine the psychometric properties of the developed SDQO. Item analysis was conducted to assess the performance of each item, and exploratory factor analysis (EFA) was employed to analyze its initial structure and eliminate less ideal items. Subsequently, confirmatory factor analysis (CFA) was used to examine the model fit of the suggested structure by EFA, using different subsamples. Internal consistency, concurrent validity, and other analyses were also performed. RESULTS: The original 27-item SDQO was reduced to 17 items after removing 4 questions in item analysis and 6 questions in EFA. The Cronbach's alpha for the 17-item version of SDQO was 0.80. Both EFA and CFA supported its 6-factor structure, with factors identified as community activities, loneliness, personal resources, leisure activities, friendship, and family resources. SDQO also demonstrated expected performance in concurrent validity. CONCLUSION: The 17-item version of SDQO exhibited good reliability and validity, measuring various aspects of social detachment behavior, feelings, and resources. It holds value for future research applications.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".