Psychometric properties, factorial structure, and measurement invariance of the English and French versions of the Medical Outcomes Study social support scale.
Bibliographic record
Abstract
BACKGROUND: The Medical Outcomes Study (MOS) social support scale is a 19-item survey that measures four dimensions of functional support. The current study reports on the psychometric properties, factorial structure, and measurement invariance of the scale for a sample of English- and French-speaking Canadians aged 55 or older. DATA AND METHODS: The internal consistency and composite reliability for a congeneric measurement model of the dimensions of functional social support were examined. A confirmatory factor analysis and test of invariance across language (English = 2,642; French = 489) were also performed. RESULTS: Across both English- and French-speaking respondents, results indicated good internal consistency (Cronbach's alpha ranged from .90 to .97) and composite reliability (ranging from .93 to .97) for all dimensions of functional social support. The confirmatory factor analysis revealed acceptable fit indices for the 4-factor structure similar to the original one. The scale appears to function uniformly across both language groups. INTERPRETATION: The MOS social support scale appears to be a psychometrically sound instrument for use in research on social support with samples of English- and French-speaking older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".