Measuring extended families over time in informal settlements in Nairobi, Kenya: Retention and data consistency in a two-round survey
Bibliographic record
Abstract
BACKGROUND: Researchers have increasingly turned to longitudinal data to understand how the family environment of children changes over time and how this change affects their well-being. While the value of such efforts is clear, the inherent challenges of collecting robust data over time may limit or bias our understanding of family complexity. OBJECTIVE: Drawing on data from an exploratory study on kinship structure and support for low income single mothers and their young children in Nairobi, Kenya, this paper aims to (1) assess the strengths and weaknesses of our approach in reflecting the complexities of kinship dynamics and (2) analyze how methodological issues such as selection and reporting inconsistency can influence our understanding of the role of kin in children's lives. METHODS: The analysis used data from two waves of the Kinship Support Tree (KST) project. The starting sample consisted of 462 single mothers with at least one child under the age of 7, with data collected on approximately 5,000 resident and nonresident kin. Descriptive statistics and conventional tests of significance were used to analyze selection factors and inconsistencies in reporting across waves. RESULTS: The study yielded a 91% retention rate after six months and the analysis provides some assurance that selectivity from attrition and reporting inconsistency are not entirely driven by shifts in support provision by kin. However, the selectivity of the sample underscores caution in generalizing the results. CONCLUSIONS: While the challenges of conducting follow-up surveys such as the KST are serious, these findings suggest that it is possible to collect consistent data on kinship structure and support from the perspective of children in a mobile population. Tracking kinship structure over time using the KST is not only feasible but more importantly is unlikely to lead to incomplete or biased understanding of kinship. CONTRIBUTION: After further testing with a wider range of women, we hope to disseminate our results for use in a wide range of contexts both in and out of Africa. We believe this data is vital to designing appropriate interventions to improve the well-being of children growing up in these communities.
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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 | 0.057 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".