UpStart Parent Survey: A New Psychometrically Valid Tool for the Evaluation of Prevention-focused Parenting Programs
Bibliographic record
Abstract
Parents are the most significant influence on the growth and development of young children. All parents can increase their knowledge of developmental milestones and parenting practices by participating in effective programs that offer information and support. However, there is limited outcome evaluation of programs offering these services. Prevention-focused parenting programs (P-FPPs) are key frontline services designed to educate parents and improve the overall well-being of children. Evaluation of these programs is currently weak; this is not to say they are ineffective, rather that their effectiveness has been poorly evaluated. Rigorous evaluation of P-FPPs would support informed funding and evidence-based policy decisions. The purpose of this study was to conduct a preliminary psychometric analysis of the UpStart Parent Survey (USPS)-a tool developed specifically for evaluating this type of program. Preliminary analysis revealed uni-dimensionality of each scale, strong internal consistency and temporal stability, as well as strong concurrent validity on 9 of the 11 items examined with an urban Canadian population. In its first round of psychometric evaluation, the USPS demonstrated promise as a brief, easy to administer, scientifically rigorous tool for the evaluation of prevention-focused parenting programs.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".