Modeling Parenting Programs as an Interim Service for Families Waiting for Children's Mental Health Treatment
Bibliographic record
Abstract
Using a discrete choice conjoint experiment, we explored the design of parenting programs as an interim strategy for families waiting for children's mental health treatment. Latent class analysis yielded 4 segments with different design preferences. Simulations predicted the Fast-Paced Personal Contact segment, 22.1% of the sample, would prefer weekly therapist-led parenting groups. The Moderate-Paced Personal Contact segment (24.7%) preferred twice-monthly therapist-led parenting groups with twice-monthly lessons. The Moderate-Paced E-Contact segment (36.3%), preferred weekly to twice-monthly contacts, e-mail networking, and a program combining therapist-led sessions with the support of a computerized telephone e-coach. The Slow-Paced E-Contact segment (16.9%) preferred an approach combining monthly therapist-led sessions, e-coaching, and e-mail networking with other parents. Simulations predicted 45.3% of parents would utilize an option combining 5 therapist coaching calls with 5 e-coaching calls, a model that could reduce costs and extend the availability of interim services. Although 41.0% preferred weekly pacing, 58% were predicted to choose an interim parenting service conducted at a twice-monthly to monthly pace. The results of this study suggest that developing interim services reflecting parental preferences requires a choice of formats that includes parenting groups, telephone-coached distance programs, and e-coaching options conducted at a flexible pace.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".