Emerging integrated care models for children and youth with mental health difficulties in Norway: a horizon scanning study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".