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Record W4281773782 · doi:10.1177/10634266221090151

Levels of Care: A Scoping Review of Conceptualizations in Child and Adolescent Mental Health Services

2022· review· en· W4281773782 on OpenAlexafffund
Catalina Sarmiento, Graham J. Reid

Bibliographic record

VenueJournal of Emotional and Behavioral Disorders · 2022
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersChildren's Health Research Institute
KeywordsPsychologyMental healthTriageQualitative researchApplied psychologyHealth careNursingPsychotherapistMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

The term level of care is utilized in mental health care to refer to the different ways in which treatment can be delivered, including qualitative (e.g., modality, approach, setting) and quantitative (e.g., number of sessions, frequency of sessions) variations in services. The concept is often embedded in models of care and measures, yet there is no agreed upon definition or understanding of what it encompasses. A scoping review of level of care conceptualizations in models of care, and triage and service use measures was conducted. Twenty-nine conceptualizations across 63 articles were identified, 12 focused on qualitative components, four on quantitative components, and 13 on a combination of qualitative and quantitative components. A conceptual model to integrate the literature reviewed is proposed.

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)0.018
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0250.028
Science and technology studies0.0010.004
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.411
Teacher spread0.338 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2022
Admission routes2
Has abstractyes

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