Development and validation of a social vulnerabilities survey for medical inpatients
Bibliographic record
Abstract
OBJECTIVES: Our objective was to validate a Social Vulnerabilities Survey that was developed to identify patient barriers in the following domains: (1) salience or priority of health; (2) social support; (3) transportation; and (4) finances. DESIGN: Cross-sectional psychometric study.Questions for one domain (health salience) were developed de novo while questions for the other domains were derived from national surveys and/or previously validated questionnaires. We tested construct (ie, convergent and discriminative) validity for these new questions through hypothesis testing of correlations between question responses and patient characteristics. Exploratory factor analysis was conducted to determine structural validity of the survey as a whole. SETTING: Patients admitted to the inpatient internal medicine service at a tertiary care hospital in Calgary, Canada. PARTICIPANTS: A total of 406 patients were included in the study. RESULTS: The mean age of respondents was 55.5 (SD 18.6) years, with the majority being men (55.4%). In feasibility testing of the first 107 patients, the Social Vulnerabilities Survey was felt to be acceptable, comprehensive and met face validity. Hypothesis testing of the health salience questions revealed that the majority of observed correlations were exactly as predicted. Exploratory factor analysis of the global survey revealed the presence of five factors (eigenvalue >1): social support, health salience, drug insurance, transportation barriers and drug costs. All but four questions loaded to these five factors. CONCLUSIONS: The Social Vulnerabilities Survey has face, construct and structural validity. It can be used to measure modifiable social vulnerabilities, such that their effects on health outcomes can be explored and understood.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".