Stress and Mental Health in Families With Different Income Levels: A Strategy to Collect Multi-Actor Data
Bibliographic record
Abstract
BACKGROUND: Several studies have focused on family stress processes, examining the association between various sources of stress and the mental health and well-being of parents and adolescents. The majority of these studies take the individual as the unit of analysis. Multi-actor panel data make it possible to examine the dynamics of the family context over time and the differentiating effects of individual roles within the same family. Accurate information about family processes allows practitioners to provide support that enhances family resilience and minimizes the risk of mental health problems. OBJECTIVE: Our study contributes to the research on family stress processes by focusing on families with different income levels, and by collecting panel data from mothers, fathers, and adolescents within the same family. METHODS: The relationship between mothers, fathers, and children (RMFC) study is an ongoing Flemish multi-actor panel study that aims to enhance our understanding of family processes that protect the mental health and well-being of two-parent families with a target adolescent between 11 and 17 years old. Mothers, fathers, and children provide information about various aspects of family life, including finances, sources of stress, health, mental health, parenting, and coping strategies. Measures have been chosen whenever possible that have sound conceptual underpinnings and robust psychometric properties. The study posed two challenges. First, economically disadvantaged families are difficult to reach. Second, the collection of multi-actor data is often plagued by high nonresponse. To ensure that the families were targeted as successfully as possible, the study employed a purposive nonprobability sampling method. RESULTS: The RMFC study is one of the largest triadic panel studies of its kind. The first wave of quantitative data collection was conducted between February 2012 and January 2013. A total of 2566 individuals of 880 families participated in our study. The second wave of data collection will be undertaken 6-12 months later. CONCLUSIONS: The strength of the RMFC study is its multi-actor panel approach of data collection among families with different income levels. Strategies that were followed to address the empirical issues involved with the sampling design are discussed, together with theoretical and practical implications.
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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 | 0.050 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".