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Enregistrement W7162019086 · doi:10.82308/5323

Determinants of Self-referral Pathways to Youth Mental Health Services and Their Impact on Timely Access to Care

2024· dissertation· en· W7162019086 sur OpenAlexaboutno aff
Nora Morrison

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineHealth Professions
ThématiqueAdolescent and Pediatric Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMental healthReferralOddsLogistic regressionCohortMental health servicePsychological interventionImputation (statistics)Sample (material)

Résumé

récupéré en direct d'OpenAlex

Aims: The traditional youth mental health system has been criticized for being inaccessible and complicated, with youth making multiple stops before receiving care. ACCESS Open Minds (ACCESS), a services research project, developed, implemented, and evaluated a transformation of youth mental health services at 14 sites across Canada. Unlike the traditional system, ACCESS allowed youth to refer themselves (self-referral). Self-referral has been theorized to shorten treatment delays, especially for traditionally underserved youth. Questions remain about which youth self-refer and if it impacts wait times to mental health services. This study aims to compare: (1) socio-demographic and clinical characteristics associated with self-referral versus other referral routes among help-seeking youth at ACCESS sites; (2) wait times to first appointment for those self-referring versus using other routes. We hypothesized that self-referral would be associated with shorter wait times to first appointment. Methods: Data on sociodemographic, clinical, referral pathways, and service use factors were collected via records, self-report forms, and clinical interviews. Eleven of the fourteen sites were included in analyses; excluded sites were either not part of the cohort study (n=2) or did not collect data on key outcomes (n=1). Multiple logistic regression and an accelerated failure time model, both with multilevel modeling, were used to investigate the first and second aim, respectively. Multivariate Imputation by Chained Equations models were used to handle missingness. Results: The analytic sample included 4,421 youth; 39% were self-referred and 61% arrived via other referral pathways. The odds of self-referral were higher for each increasing year of age (OR:1.10, 95% CI:1.06-1.14), for those who did not have a secondary diploma, compared to those who were too young to have a secondary diploma (OR:1.42, 95% CI:1.02-1.98); for those who had previously been assessed at ACCESS sites, compared to those with no previous service seeking (OR:2.28, 95% CI:1.61-3.24), and for each 6 month increase in time since ACCESS implementation (OR:1.09, 95% CI:1.05-1.14). Conversely, sexual minority youth (OR:0.81, 95% CI:0.67-0.98) and those with moderate-to-significant difficulties with functioning (OR:0.81, 95% CI:0.65-0.99) were less likely to self-refer. Self-referral was not associated with gender, ethnic or cultural origins, engagement in education, employment, or training, presence of a reliable adult, mental health problem severity, or coming in before or after the start of the COVID-19 pandemic. Controlling for these socio-demographic, clinical, and service use factors, self-referral was associated with decreased time (in days) from referral to first appointment (TR:0.70, 95% CI:0.65-0.76). Explanatory analyses showed that the increased time to first appointment for those referred by others is attributable to the time needed to first contact the referral source and then the youth to offer an appointment. Conclusions: The notable uptake of self-referral and its impact on increasing timeliness of access to care suggest that self-referral should remain a feature of youth mental health service reform. However, this pathway was used differentially by youth based on certain characteristics, which may contribute to disparities in timely access to care. Future work should promote self-referral, particularly among those less likely to use it, while reducing delays to appointments for youth referred by others

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)0,002
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,119
Score d'incertitude au seuil0,236

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,015
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,089
Tête enseignante GPT0,465
Écart entre enseignants0,376 · 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'étudeObservationnel
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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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