Improving routine mental health screening for depression and anxiety in a paediatric lupus clinic: a quality improvement initiative for enhanced mental healthcare
Bibliographic record
Abstract
BACKGROUND: Mental health (MH) conditions are prevalent in adolescents with childhood-onset SLE (cSLE). Early identification is crucial in preventing poor patient outcomes; however, MH screening rates remain low. LOCAL PROBLEM: From July 2021-January 2022, only 15% of adolescents in a paediatric tertiary care cSLE clinic were being screened for depression and anxiety. By November 2023, we aimed to increase the percentage of patients with cSLE (≥12-18 years) screened for depression (Patient Health Questionnaire: PHQ-9) and anxiety (Generalised Anxiety Disorder-7: GAD-7) from 15% to 80%. METHODS: This quality improvement project employed the Model for Improvement framework. Stakeholders included the clinic team, patients and families, and MH providers. Statistical process control charts were used to analyse the outcome measure for percentage of screened patients with cSLE. Patient and caregiver satisfaction surveys were conducted at baseline and after screening as a balancing measure. INTERVENTIONS: MH screening workflow with a referral algorithm was developed with stakeholders. Additional interventions included two MH training workshops for healthcare providers and a preclinic reminder of eligible patients for screening. RESULTS: Over 21 months, 146 patients with cSLE completed 270 MH screens, increasing the screening rate from 15%, peaking at 100%, to a median of 56%. Sixty-six individuals (45%) reported symptoms of depression and/or anxiety on their initial screen. Of 270 screens, 44 individuals (17%) reported moderate to severe symptoms meeting the screening workflow criteria for referral to a MH service; 10% of patients screened were referred and seen by the MH service within 2-12 weeks. Patients and caregivers reported satisfaction with the MH screening process and quality of MH follow-up. CONCLUSION: Despite not sustainably meeting the target, MH screening rates increased in the cSLE clinic by nearly fourfold, demonstrating feasibility and acceptability. Patients expressed satisfaction with their mental health follow-up, emphasising its importance in their care.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".