The Future of Parenting Programs: I Design
Bibliographic record
Abstract
SYNOPSISParenting programs worldwide (and especially in low- and middle-income countries) support parents in their caregiving roles. Parenting programs are popular and prolific, but many outright fail to deliver meaningful effects or eventuate in only small effects. Incomplete consideration and execution of many design features of programs can account for these shortfalls. This article delimits several critical criteria surrounding successful design and evaluation of evidence-based parenting programs. Specific factors include important preliminary questions concerning details of program design, such as whether the topic of the parenting program specifies the aspect(s) of parenting to be encouraged or discouraged and what theory of change underlies the program; program design contents concern subject matter development, sources, and messages; program design components specify the delivery mode, effectiveness, location, and alignment; program design targeting and sampling concern whom the program is addressing, why, and whether the program is designed to be universal or targeted to a specific population; ensuring reliable and valid program measurement; and rigorous experimental standards that encompass evaluating program effectiveness, including randomized control trial or quasi-experimental designs and the selection of control and comparison conditions. Policy makers, program leaders, investigators, and, of course, parents and children all benefit when parenting programs are well designed.Objective.Design.Results.Conclusions.
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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 | 0.049 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".