Implementation of the Mind Youth Questionnaire (MY-Q) for routine health-related quality of life screening of adolescents with type 1 diabetes in a large tertiary care center
Bibliographic record
Abstract
OBJECTIVES: Prevalence of diabetes distress and mental health comorbidities among adolescents with type 1 diabetes (T1D) is high. Despite recommendations for routine psychosocial risk assessment, there is little guidance for their implementation. This study aims to describe the implementation and baseline outcomes of the Mind Youth Questionnaire (MY-Q), a validated psychosocial screening tool for health-related quality of life (QoL) including mood, among adolescents living with T1D. METHODS: Adolescents aged 13-18 years completed the MY-Q from October 1, 2019-April 1, 2023. Baseline characteristics, MY-Q results including categories flagged positive (noting possible areas of concern), debrief duration, and frequency of social work or mental health referral were collected and analyzed using descriptive statistics. RESULTS: A total of 343 adolescents (mean age 15.3 years; 52 % female) completed a baseline MY-Q. Median overall MY-Q debrief time (IQR) was 10.0 min (6.0, 20.0). About 290 (84.5 %) adolescents had at least one of seven categories flagged, most commonly "Family" (61 %). About 30 % of adolescents had "Mood" flagged, and 2.9 % of adolescents were referred to mental health following debrief. CONCLUSIONS: Without the need for additional resources, implementation of the MY-Q in a pediatric tertiary care diabetes clinic successfully identified QoL issues and mental health concerns among adolescents with T1D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".