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Enregistrement W4411329562 · doi:10.3310/gydw4507

Enhancing referrals to Child and Adolescent Mental Health Services: the EN-CAMHS mixed-methods study

2025· article· en· W4411329562 sur OpenAlexaboutno aff
Kathryn M. Abel, Pauline Whelan, Lesley‐Anne Carter, Heidi Tranter, Charlotte Stockton-Powdrell, Kerry Gutridge, Lamiece Hassan, Rachel Elvins, Julian Edbrooke‐Childs

Notice bibliographique

RevueHealth and Social Care Delivery Research · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueAdolescent and Pediatric Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesHealth Services and Delivery Research Programme
Mots-clésMental healthReferralMedicineQuarter (Canadian coin)National Service FrameworkPsychiatryService (business)PsychologyNursingFamily medicine

Résumé

récupéré en direct d'OpenAlex

Background: National Health Service Child and Adolescent Mental Health Services are specialist teams that assess and treat children and young people with mental health problems. Overall, 497,502 children were referred to National Health Service Child and Adolescent Mental Health Services between 2020 and 2021, and almost one-quarter of these referrals were not successful. Unsuccessful referrals are often distressing for children and families who are turned away usually after a long waiting period and without necessarily being redirected to alternative services. The process is also costly to services because time is wasted reviewing documents about children who should have been referred for alternative help and may prevent young people who need specialist help receiving it in a timely way. The overarching aim of this study was to understand what the problems are with Child and Adolescent Mental Health Services referrals and identify solutions that could improve referral success. A key objective was to talk widely with young people and families, people working in Child and Adolescent Mental Health Services and mental health professionals so that we could understand fully what the problems were and how we might develop their solutions. We gathered individual pseudonymised patient data from nine Child and Adolescent Mental Health Services, and referral data from four National Health Service Trusts to look at what data are available and how complete it is. We report wide variation in the numbers of referrals between and within Trusts and in the proportions not being successful for treatment. Data on factors such as age and gender of children and young people referred into Child and Adolescent Mental Health Services and who made the referral are routinely collected, but ethnicity of the children and young people's reason for referral are not as well collected across all Trusts. We also conducted focus groups with over 100 individuals with differing perspectives on the Child and Adolescent Mental Health Services referral process (children and young people, parents and carers, key referrers, and Child and Adolescent Mental Health Services professionals) and asked about current difficulties within the referral process, as well as potential solutions to these. Conclusions: Problems identified included: confusion about what Child and Adolescent Mental Health Services is for, that is what it does and does not provide; and lack of support provided during the referral process. Possible solutions included: streamlining the referral pathways through digital technologies with accompanying standardisation of referral forms for National Health Service Child and Adolescent Mental Health Services; and early ongoing communication throughout the referral 'journey' for the referrer/family. Future work: Should consider the standardisation of and improvement to the Child and Adolescent Mental Health Services referral process following the recommendations outlined in this project. Study registration: This study is registered on ClinicalTrials.gov with the identifier: NCT05412368. https://clinicaltrials.gov/study/NCT05412368. Funding: ; Vol. 13, No. 21. See the NIHR Funding and Awards website for further award information.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,106

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,016
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,001
Communication savante0,0020,003
Science ouverte0,0020,003
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,117
Tête enseignante GPT0,570
Écart entre enseignants0,453 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission1
Résumé présentoui

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