The Safe Environment for Every Kid Model: Analyzing Demographics Related to Higher Risks of Child Maltreatment in High-risk Pediatric Population
Bibliographic record
Abstract
Background: The Safe Environment for Every Kid (SEEK) screener was developed by Dr. Howard Dubowitz for primary care clinicians to identify and address risk factors for child maltreatment. The instrument queries parents/guardians regarding their families' needs (e.g. food insecurity) and screens for depressive symptoms in the parent/guardian. Objective: To examine the frequency of social determinants of health (e.g., food insecurity) and its correlates using the SEEK screener during pediatric visits at a Federally Qualified Health Center in south central Ohio. Design/Methods: The SEEK screener was distributed to parents/guardians (PG) at Rocking Horse Community Health Center, a Federally Qualified Health Center in Springfield, Ohio, from April 2016 through May 2019. Inclusion criteria were English speaking PG who brought index children (age <19 years) in for an appointment with one of the investigators (JP). Many of the index children had learning or behavior challenges identified by the PG during the visit. 265 PG of the index children completed the survey. Comparisons between demographic characteristics and SEEK variables were made with chi-square tests. Results: The response rate was over 95%; 20 surveys were excluded for this analysis due to incomplete data. The median age of index children was 6 years, 68.1% were white, 86.5% had public health insurance and 52.7% lived with either their mother or father as a single parent. One third of respondents reported annual household income under $10,000, almost one third reported food insecurity, and over one quarter (27.5%) reported feeling down or depressed in the past month. PG of younger children (<6 years) were more likely to report depressive symptoms compared to PG of older children (34.1% vs 22.7%, p=0.048). In contrast, food insecurity was stable across all ages at about one third of the sample. Both depressive symptoms and food insecurity had a strong relation to type of health insurance: 9.4% of PG with private health insurance had depressive symptoms compared to 31.6% with public insurance (p=0.010), and depressive symptoms were reported by 12.1% with private insurance vs. 36.2% with public insurance (p=0.006). Conclusion(s): The SEEK screener is a very useful tool to identify important factors that affect children's well-being, their families' unmet social needs and PG depressive symptoms. While adult depressive symptoms are more common in PG of younger children, food insecurity impacts low income families across their life cycle.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".