Global Assessment of Relational Functioning: A Dynamic Family Measure Predicting Outcome in Children With Diabetes
Bibliographic record
Abstract
While the prevalence of type 1 diabetes (T1D) in the pediatric population has been increasing dramatically in recent years, most youths with T1D do not meet the treatment targets recommended by the American Diabetes Association. The multiple self-report scales for parents and adolescents that have been investigated in relation to treatment adherence and glycemic control in pediatric T1D show limited predictive abilities. This longitudinal observational study investigates whether the Global Assessment of Relational Functioning (GARF) can predict the medical outcome for newly diagnosed youths with T1D. The GARF is a brief structured interview assessing important areas of family functioning. The GARF assesses three main areas of family functioning: The organization, the emotional climate, and the problem-solving attributes of the family. Fifty-one youths recently diagnosed with diabetes and their families were recruited from a care facility in Canada. The age of the youths ranged from 1 to 16 years (M = 8.89; SD = 4.2), comprising 13 preschoolers, 28 school-aged children, and 10 teenagers. Including family members, a total of 139 people participated in the assessments. Correlations were sought between GARF scores, patients' serum glycosylated hemoglobin (HbA1c) and the frequency of ER visits, hospitalizations, episodes of ketoacidosis, severe hypoglycemia, insulin resistance, and mental health referrals over 21 months. The GARF score was significantly inversely correlated with outcome HbA1c scores (r = -0.61, p < 0.001), indicating that higher family functioning is associated with better metabolic control. These results suggest the GARF could be administered at diagnosis to predict diabetes outcome among a pediatric population.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".