Measuring parent attributes and supervision behaviors relevant to child injury risk: examining the usefulness of questionnaire measures
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify self report questionnaire measures of parent attributes and behaviors that have relevance for understanding injury risk among children 2-5 years of age, and test a new Parent Supervision Attributes Profile Questionnaire (PSAPQ) that was developed to measure aspects of protectiveness and parent supervision. METHODS: Naturalistic observations were conducted of parents' supervision of children on playgrounds, with questionnaires subsequently completed by the parent to measure parent education, family income, parent personality attributes, attributes relevant to parent supervision, and beliefs about parents' control over the child's health status. These measures were then related to children's risk taking and injury history. RESULTS: Visual supervision, auditory supervision, and physical proximity were highly intercorrelated, indicating that parents employed all types of behaviors in service of supervision, rather than relying predominantly on one type of supervisory behavior. Physical proximity was the only aspect of supervision behavior that served a protective function and related to children's risk taking behaviors: parents who remained close to their children had children who engaged in less risk taking. On questionnaires, parents who reported more conscientiousness, protectiveness, worry about safety, vigilance in supervision, confidence in their ability to keep their child safe, and belief in control over their child's health had children who showed less risk taking and/or experienced fewer injuries. The new PSAPQ measure was associated with specific aspects of supervision as well as children's risk taking and injury history. CONCLUSIONS: This study reveals several parent attributes and behaviors with relevance for child injury risk that can be measured via self report questionnaires, including the new PSAPQ.
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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 | 0.015 | 0.046 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".