Examining the Importance of Family History in Pediatric Behavioural Referrals
Bibliographic record
Abstract
In recent years, there has been a substantial increase in the diagnosis of attention deficit hyperactivity disorder (ADHD) in children. Without appropriate management of symptoms and care, ADHD has been associated with a variety of negative child and adult outcomes. Environmental and familial factors that may contribute to three different pediatric referral types (academic, behavioural, and attentional) associated with ADHD were examined in the current study. In total, data from 477 families who were interviewed as a part of the intake process to a pediatric clinic were included in this study. Data for the current study was extracted from the intake questionnaires and included information on family history of mental health issues, socioeconomic status, and family relationships. The sample included children between the ages of three and 17 and mostly comprised males (n = 340). A frequency analysis of the data demonstrated relatively high rates of mental health issues within families (61.4%); almost half of the mothers reported some post-secondary education (46.1%) and most reported having normal relationships with their children (mothers, 78%; fathers, 62.9%). Finally, three stepwise regression analyses were conducted to predict referral type. All three regressions yielded significant models. Fifteen percent of the variability of the academic referral type was predicted by being male, age at the time of referral, mother’s education level, and mother’s learning. The behavioural referral types were predicted by a family history of depression, being male, mother-child relationship, and age at the time of referral; these accounted for 23% of the variance. Attentional referral type was predicted only by mother-child relationship that captured 6% of the variance. Overall, this study describes a population of parents of children with academic, behavioural, and attention-related referrals to pediatrics. Results indicate that mothers have a profound influence on their child’s referral types, something that may transfer into later diagnosis and perhaps prognosis. Clinicians and researchers alike should focus their efforts toward developing integrative service assessment and treatment approaches that include important people in the child’s life. The implementation of Community Social Pediatrics (a streamlined, inclusive approach to care) should be considered in urban centres like this one, where referrals like this are prevalent.
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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 | 0.017 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".