Optimizing Coaching During Web-Based Relationship Education for Low-Income Couples: Protocol for Precision Medicine Research
Bibliographic record
Abstract
BACKGROUND: In-person relationship education classes funded by the federal government tend to experience relatively high attrition rates and have only a limited effect on relationships. In contrast, low-income couples tend to report meaningful gains from web-based relationship education when provided with individualized coach contact. However, little is known about the method and intensity of practitioner contact that a couple requires to complete the web-based program and receive the intended benefit. OBJECTIVE: The aim of this study is to use within-group models to create an algorithm to assign future couples to different programs and levels of coach contact, identify the most powerful predictors of treatment adherence and gains in relationship satisfaction within 3 different levels of coaching, and examine the most powerful predictors of treatment adherence and gains in relationship satisfaction among the 3 levels of coach contact. METHODS: To accomplish these goals, this project intends to use data from a web-based Sequential Multiple Assignment Randomized Trial of the OurRelationship and web-based Prevention and Relationship Enhancement programs, in which the method and type of coach contact were randomly varied across 1248 couples (2496 individuals), with the hope of advancing theory in this area and generating accurate predictions. This study was funded by the US Department of Health and Human Services, Administration for Children and Families (grant number 90PD0309). RESULTS: Data collection from the Sequential Multiple Assignment Randomized Trial of the OurRelationship and web-based Prevention and Relationship Enhancement Program was completed in October of 2020. CONCLUSIONS: Some of the direct benefits of this study include benefits to social services program administrators, tailoring of more effective relationship education, and effective delivery of evidence- and web-based relationship health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33047.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.017 |
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".