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Record W4321258688 · doi:10.1093/pch/18.2.81

The current state of mental health services in Canada's paediatric emergency departments

2013· article· en· W4321258688 on OpenAlexaffabout
Stephanie L. Leon, Mario Cappelli, Samina Ali, William Craig, Janet Curran, Rebecca Gokiert, Terry P. Klassen, Martin H. Osmond, Shannon D. Scott, Amanda S. Newton

Bibliographic record

VenuePaediatrics & Child Health · 2013
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of ManitobaUniversity of AlbertaDalhousie UniversityUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMental healthMedicineIntervention (counseling)GuidelineHealth careChild and adolescent psychiatryFamily medicineNursingEmergency departmentPsychiatryPolitical science

Abstract

fetched live from OpenAlex

To describe emergency mental health services in major paediatric centres across Canada. A cross-sectional study of mental health services in emergency departments (EDs) from all 15 Canadian tertiary care paediatric centres was conducted. Fifteen individuals participated and were either a paediatric emergency physician with administrative responsibilities (60%) or an emergency mental health care provider (40%). Four participants reported that their ED used an evidence-based guideline, tool or policy, and one participant reported their ED based its services on published research evidence. Reported ED-based mental health resources included a crisis intervention team (five EDs), a mental health nurse (six EDs) and a social worker (five EDs). Thirteen participants reported on-site consultation with child psychiatry and six reported urgent follow-up as an adjunct service to ED care. There is a wide variety of mental health care practices in Canadian paediatric EDs. Consideration of which resources are required to ensure evidence-based, effective services are provided to children and youth is necessary.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.311
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.277
Teacher spread0.269 · 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 teacher head, 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

Citations41
Published2013
Admission routes2
Has abstractyes

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