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Record 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 on OpenAlexaboutno aff
Nora Morrison

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthReferralOddsLogistic regressionCohortMental health servicePsychological interventionImputation (statistics)Sample (material)

Abstract

fetched live from 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

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.089
GPT teacher head0.465
Teacher spread0.376 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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