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1701 Improving completion rates of routine mental health screening for depression and anxiety in paediatric lupus outpatient clinic to enhance patient mental health care

2022· article· en· W4313532685 on OpenAlexaff
Tala El Tal, Avery Longmore, Abdulaziz Al Mutairi, Audrea Chen, Holly Convery, Dinah Finkelstein, Linda T. Hiraki, Chetana Kulkarni, Justine Ledochowski, Neely Lerman, Karen Leslie, Deborah L. Levy, Sharon Lorber, Jayne MacMahon, Jeanine McColl, Sarah I. Mossad, Oscar Mwizerwa, Lawrence C. Ng, Luana F. Pereira, Vandana Rawal, Alaa Shehab, Amani Al Bijadi, Evelyn Smith, Alène Toulany, Andrea Knight

Bibliographic record

VenuePharmacoepidemiology · 2022
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineAnxietyDepression (economics)Mental healthOutpatient clinicPatient Health QuestionnaireBeck Anxiety InventoryReferralPatient satisfactionBeck Depression InventoryPsychiatryPhysical therapyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Background/Purpose Mental health (MH) problems are prevalent in adolescents with childhood-onset lupus (cSLE), with cross-sectional studies estimating prevalences of 20-60% for depression symptoms and 20-40% for anxiety symptoms. Despite this, MH screening rates are low. Identifying and treating MH symptoms early on is crucial as they are known to be associated with poor patient outcomes. A six-month chart audit (July 2021- Dec 2021) revealed a baseline median percentage of 17% of cSLE patients with documented MH screening in paediatric lupus outpatient clinic at The Hospital for Sick Children (Sickkids). In response, we aimed to: 1) increase percentage of cSLE patients (≥ 12-18 yo) with routine MH screening for depression (Patient Health Questionnaire-9 (PHQ-9)) and anxiety (Generalized Anxiety Disorder-7 (GAD-7)) from 17% to 80%, and if positive, 2) increase percentage of documented initial management (psychoeducation and/or referral to appropriate MH service(s)) from 22% to 80% in cSLE outpatient clinic by Sep 2022. Methods This is a time series study analyzed with run charts. Root cause analysis was performed using fishbone diagram, 5Whys, and pareto chart. Patient and parent satisfaction surveys were conducted to determine their baseline satisfaction. Plan-Do-Study Act (PDSA) method was used to systematically evaluate and adjust process in real time. Family of measures included outcome measure – percentage of positively screened cSLE patients with documented initial MH management, process measure – percentage of eligible cSLE patients screened, and balancing measure – number of referrals to MH services, and time till seen. Results Root causes identified included limited MH resources, lack of integration into clinic workflow, lack of standardized clinic algorithm for positive screens, lack of MH training of health care providers, and patient/family stigma and misconceptions. A series of site-specific change ideas (figure 1) were developed accordingly and implemented including 1) patients self-screened instead of administered by health care providers, 2) a standardized clinic algorithm, and 3) two 2-hour MH training workshops for health care providers. Over 50% of patients (n= 23) and parents (n=18) surveyed felt comfortable with routine MH screening, preferably in-person, and supported ongoing MH inquiry at future visits (figure 2). Patients emphasized privacy and confidentiality. Over six month period, 42 cSLE patients completed PHQ-9 and GAD-7 screens, increasing screening rate from 17 to 67%, of which 18 (43%) and 15 (36%) had positive screens respectively (figure 3). Of those, 10% (n=4) had moderate to severe scores and suicidal ideation. Six cSLE patients were referred and seen by appropriate MH service within 4-6 weeks. Majority screened (n=41) received psychoeducation and MH handout. Conclusion Routine formal depression and anxiety screening is feasible in a busy subspecialty clinic. Next steps include ongoing screening, and ensuring appropriate follow-up plan for positive screens.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.058
GPT teacher head0.446
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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