Development and validation of the Socioeconomic Status Composite Scale (SES-C)
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status (SES) is a critical multifactorial determinant of health and plays a significant role in shaping an individual's health outcomes. While a composite scale has been proposed to measure SES in children, to our knowledge, limited composite scales were developed for adults in different contexts, highlighting the need for a comprehensive and valid SES measure to elucidate the relationship between SES and health in this population. OBJECTIVE: This study aimed to develop and validate a composite scale that measures the socioeconomic status in Lebanon and assess its correlates in a socioeconomic crisis context. METHODS: An online study was carried out between October and November 2022 across all Lebanese regions. Snowball sampling was used to enroll 448 adults living in Lebanon through a questionnaire created on Google Forms and shared by WhatsApp to a first sample from all geographic areas. RESULTS: The developed composite scale (SES-C) was found to be reliable and valid. It was based on several aspects of socioeconomic status, i.e., participant education level, family head education level, perceived social class, not being in debt, not receiving financial help, crowding index, participant work status, family head work status, monthly household income, and financial well-being. Furthermore, high SES was significantly associated with married status, older age, alcohol consumption, the absence of chronic disease, easy access to healthcare, private insurance coverage, and the number of rooms in the house in the bivariate analysis. In the multivariable analysis, high SES was significantly associated with age (ORa-1.13; p = 0.011) and easy access to healthcare (ORa = 7.81; p = 0.001) and inversely associated with chronic disease (ORa = 0.17; p = 0.002). Similar results with lower magnitude were found for moderate SES. CONCLUSION: The study successfully developed and validated a composite scale (SES-C) for measuring the socioeconomic status in Lebanon, taking into account the complexities of the Lebanese context. The scale was found to be reliable and valid, and its results showed significant correlations with various factors such as older age, lower risk of chronic disease, and easy access to healthcare.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".