Adaptation of a social vulnerability index for measuring social frailty among East African women
Bibliographic record
Abstract
BACKGROUND: The number of older women living with HIV in Africa is growing, and their health outcomes may be adversely impacted by social frailty, which reflects deficits in social resources that accumulate over the lifespan. Our objective was to adapt a Social Vulnerability Index (SVI) originally developed in Canada for use in a study of older women living with or without HIV infection in Mombasa, Kenya. METHODS: We adapted the SVI using a five-step process: formative qualitative work, translation into Kiswahili, a Delphi procedure, exploration of potential SVI items in qualitative work, and a rating and ranking exercise. Four focus group discussions (FGD) were conducted (three with women living with HIV and one with HIV-negative women), and two expert panels were constituted for this process. RESULTS: Themes that emerged in the qualitative work were physical impairment with aging, decreased family support, a turn to religion and social groups, lack of a financial safety net, mixed support from healthcare providers, and stigma as an added burden for women living with HIV. Based on the formative FGD, the expert panel expanded the original 19-item SVI to include 34 items. The exploratory FGD and rating and ranking exercise led to a final 16-item Kenyan version of the SVI (SVI-Kenya) with six domains: physical safety, support from family, group participation, instrumental support, emotional support, and financial security. CONCLUSIONS: The SVI-Kenya is a holistic index to measure social frailty among older women in Kenya, incorporating questions in multiple domains. Further research is needed to validate this adapted instrument.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".