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Record W4385810580 · doi:10.1186/s12913-023-09858-x

Emerging integrated care models for children and youth with mental health difficulties in Norway: a horizon scanning study

2023· review· en· W4385810580 on OpenAlexaff
Ida Charlotte Holmen, Sina Waibel, Oddvar Kaarbøe

Bibliographic record

VenueBMC Health Services Research · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
FundersNorges ForskningsrådUniversitetet i Oslo
KeywordsMental healthReferralHealth informaticsNursing researchNursingHealth administrationScale (ratio)Health careData collectionPublic healthIntegrated careMedicinePsychologyApplied psychologyMedical educationPsychiatrySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The implementation of Integrated Care Models (ICMs) represents a strategy for addressing the increasing issues of system fragmentation and improving service customization according to user needs. Available ICMs have been developed for adult populations, and less is known about ICMs specifically designed for children and youth. The study objective was to summarize and assess emerging ICMs for mental health services targeting children and youth in Norway. METHODS: A horizon scanning study was conducted in the field of child and youth mental health. The study encompassed two key components: (i) the identification of ICMs through a review of both scientific and grey literature, as well as input from key informants, and (ii) the evaluation of selected ICMs using semi-structured interviews with key informants. The aim of the interviews was to identify factors that either promote or hinder the successful implementation or scale up of these ICMs. RESULTS: Fourteen ICMs were chosen for analysis. These models encompassed a range of treatment philosophies, spanning from self-care and community care to specialized care. Several models placed emphasis on the referral process, prioritizing low-threshold access, and incorporating other sectors such as housing and child welfare. Four of the selected models included family or parents in their target group and five models extended their services to children and youth beyond the legal age of majority. Nine experts in the field willingly participated in the interview phase of the study. Identified challenges and facilitating factors associated with implementation or scale up of ICMs were related to the Norwegian healthcare system, mental health care delivery, as well as child and youth specific factors. CONCLUSION: Care delivery targeting children and youth's mental health requires further adaptation to accommodate the intricate nature of their lives. ICMs have been identified as a means to address this complexity by offering accessible services and adopting a holistic approach. This study highlights a selection of promising ICMs that appear capable of meeting some of the specific needs of children and youth. However, it is recommended to subject these models to further assessment and refinement to ensure their effectiveness and the fulfilment of their intended outcomes.

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)0.012
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.170
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.569
GPT teacher head0.678
Teacher spread0.109 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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