Caregiver Supervision and Child-Injury Risk: I. Issues in Defining and Measuring Supervision; II. Findings and Directions for Future Research
Bibliographic record
Abstract
OBJECTIVE: To discuss the role of caregiver supervision in child-injury risk, with attention given to definitional and methodological issues and outlining important questions to be addressed in future research. METHODS: Analysis, synthesis, and critique of existing literature. RESULTS: Comparisons across studies are difficult because of insufficient specificity regarding what constitutes supervision. Hence, a multi-dimensional definition of supervision is developed based on the literature. Numerous issues arise when attempting to measure supervision and these are extensively discussed, along with reporting on the recent development of two questionnaire measures of supervision (Beliefs About Supervision Questionnaire and Parent Supervision Attributes Profile Questionnaire) that have shown good validity and hold promise for addressing the problem of measuring caregiver supervision in reliable and valid ways. A review of the findings on relations between supervision and child-injury risk reveals that many substantive questions remain unanswered. A number of recommendations for future research are given and a conceptual model is presented that focuses attention on the need for research that examines how factors interact to influence child-injury risk. This model has relevance not only for research but also for prevention and serves to emphasize the complementary nature of environment-oriented and person-oriented approaches to child-injury prevention. CONCLUSION: Direct evidence linking supervision to child-injury risk is scarce and many important questions remain unanswered. Based on the conceptual model presented, in future research it is important to examine how supervision interacts with other key factors to influence children's risk of 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 | 0.034 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".