Validation of a brief version of the Social Provisions Scale using Canadian national survey data
Bibliographic record
Abstract
INTRODUCTION: The 10-item Social Provisions Scale (SPS-10) has been implemented to measure social support in a number of national surveys in Canada. The objective of this study was to reduce the SPS-10 to a brief, five-item scale (SPS-5), while maintaining adequate measurement properties. METHODS: Data from individuals aged 18 years and older who responded to the Social Provisions Scale module in the Canadian Community Health Survey 2012 Mental Health Focus cycle (CCHS 2012 MH) and the Canadian Community Health Survey 2017 Annual cycle (CCHS 2017) were analyzed. We used exploratory factor analysis and item-to-total correlations from the CCHS 2012 MH data to choose items. A correlation analysis between the SPS-5, SPS-10 and related positive mental health (PMH) constructs were used to assess the criterion-related validity of the SPS-5 compared to the SPS-10. A confirmatory factor analysis using data from the CCHS 2017 was conducted to confirm the factor structure of the SPS‑5. RESULTS: The SPS-5 showed high internal consistency (Cronbach's alpha of 0.88) and similar correlations as the SPS-10 with related PMH constructs. The SPS-5 and SPS-10 were also very highly correlated (r = 0.97). The confirmatory factor analysis demonstrated that a single factor model of the SPS-5 fit the data well. The SPS-5 and SPS-10 yield similar estimates of high social support, of 92.7 and 91.5%, respectively. CONCLUSION: The new SPS-5 demonstrated adequate measurement properties, and functioned in a similar manner to the SPS-10, supporting a reduced version of the Scale. The SPS-5 is a feasible and valid alternative to the SPS-10 that could be used to reduce respondent burden on national health surveys.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".