A model of implementation for the family check-up in community mental health settings
Bibliographic record
Abstract
Introduction: The Family Check-Up (FCU) is an evidence-based treatment program that has been developed in over 25 years of rigorous clinical research. The FCU is associated with long-term outcomes including improved mental and behavioral health that lead to lifelong adjustment and has been implemented in various countries, including the United States of America, Sweden, Canada, and the Netherlands. Method: In this paper, we review the theoretical model that guided our research, the clinical model for service delivery, and the implementation model that guides our dissemination of the FCU in diverse community service settings in the United States. The FCU is grounded in a developmental, ecological model in which contextual stressors predict parenting skills and family relationships, which are key mediators and targets of the intervention. The FCU in-person program includes a 3-session, strength-based assessment that culminates in a feedback session which then leads to tailored parenting support for families. Our community implementation model occurs in 4 phases that include exploration of community need, consultation, training, and ongoing support for sustainment. A combination of e-learning, virtual trainings, provider consultation and certification, and supervisor training is included in the implementation model. Results: Results across studies demonstrated improvements in parent self-efficacy, stress, emotion regulation, anxiety, depression, and parenting behaviors (positive and proactive parenting, limit-setting), and reductions in negative parenting, family conflict, and child emotional problems. Conclusion: Our goal is to expand the reach of mental health prevention around the world by increasing dissemination of the FCU in community settings through a collaborative, community-engaged process, and integrating our new digital health program into a range of mental health service settings.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".