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Record W4403682387 · doi:10.1093/pch/pxae067.004

04 Care for adolescents with substance use: A national survey of paediatric hospitals

2024· article· en· W4403682387 on OpenAlexaboutno aff
Karen Leslie, Stephanie Hosang, Nicholas Chadi, Christina Grant, Dzung X. Vo, Eva Moore, Richard E. Bélanger, Trish Tulloch, Natalie Finner, Rachel Goren

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsSubstance useMedicineFamily medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Substance use is a recognized Paediatric health care issue. More than 9 in 10 individuals receiving treatment for substance use disorders report that their first use of substances occurred by young adulthood. The Canadian Institute for Health Information reported that 5% of hospital stays among Canadian youth in 2017-2018 were related to harmful substance use. In a study of youth with a first-time emergency department visit for a mental health problem between 2010 and 2014, most visits were due to substance related disorders. Despite this, little is known about practices relating to substance use in children's hospitals. Objectives 1. To describe how adolescents who present to Paediatric hospitals with substance use disorders or substance-related health issues are identified, assessed, and treated. 2. To identify best-practices that can inform national standards of care. Design/Methods An online questionnaire was utilized to obtain information on: 1. Clinical practices in the emergency department, inpatient services and ICU; 2. Hospital policies and practice guidelines; and 3. Hospital and community programs and resources. Recruitment emails were sent to hospital leaders (CEO or equivalent) at all 13 Paediatric hospitals. Leaders were asked to identify one individual to complete the survey on behalf of their hospital with input from relevant stakeholders in the various clinical areas. The survey was developed iteratively by a national group of 9 adolescent medicine clinicians and 2 Paediatric trainees. The survey was piloted at a single Paediatric hospital and refined. The survey was administered via RedCap technology. Results Survey response rate was 70% (9/13 hospitals). The mean number of contributors to each survey was 9 (range of 1-35). Overall, few hospitals utilized best practices consistently. There was inconsistency in the use of validated screening tools, use of clinical practice guidelines for monitoring and ongoing care and prescribing of nicotine replacement therapy and opioid agonist therapy. These inconsistencies were noted within each clinical setting (emergency department, inpatient services, and ICU), across services in each hospital, and between hospitals. With respect to hospital services, 5/9 had some type of consult team related to substance use (e.g., adolescent medicine), 4/9 had specialized substance use outpatient services, and 2/9 offered partial hospitalization/day treatment for patients with substance use issues. Local community-based substance use resources were identified as insufficient to address needs by 8/9 hospitals. Conclusion Inconsistencies in the care of adolescents with substance use disorders or substance-related health issues highlights the need for national guidelines for Paediatric hospital-based assessment and care, and enhanced coordination between services and systems of care for this patient population.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.365
Teacher spread0.322 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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