The Parent Supervision Attributes Profile Questionnaire: a measure of supervision relevant to children’s risk of unintentional injury
Bibliographic record
Abstract
OBJECTIVE: To further establish the psychometric properties of the Parent Supervision Attributes Profile Questionnaire (PSAPQ), a questionnaire measure of parent supervision that is relevant to understanding risk of unintentional injury among children 2 through 5 years of age. METHODS: To assess test-retest reliability, parents completed the PSAPQ twice, with a one month interval. Internal consistency estimates for the PSAPQ were also computed. Confirmatory factor analyses were applied to the data to assess the four factor structure of the instrument by assessing the convergent and divergent validity of the subscales and their respective items. RESULTS: Test-retest reliability and internal consistency scores were good, exceeding 0.70 for all subscales. Factor analyses confirmed the hypothesized model--namely that the 29 item questionnaire comprised four unique factors: protectiveness, supervision beliefs, risk tolerance, and fate influences on child safety. CONCLUSIONS: Previous tests comparing the PSAPQ with indices of actual supervision and children's injury history scores revealed good criterion validity. The present assessment of the PSAPQ revealed good reliability (test-retest reliability, internal consistency) and established the convergent and divergent validity of the four factors. Thus, the PSAPQ has proven to have strong psychometric properties, making it a unique and useful measure for researchers interested in studying links between supervision and young children's risks of unintentional injury.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".