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Record W2954748702 · doi:10.1111/eip.12813

Transforming youth mental health services in a large urban centre: ACCESS Open Minds Edmonton

2019· article· en· W2954748702 on OpenAlexafffundabout
Adam Abba‐Aji, Katherine Hay, Jill Kelland, Christine L. Mummery, Liana Urichuk, Cindy Gerdes, Mark Snaterse, Pierre Chue, Shalini Lal, Ridha Joober, Patricia Boksa, Ashok Malla, Srividya N. Iyer, Jai Shah

Bibliographic record

VenueEarly Intervention in Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversité de MontréalUniversity of AlbertaMcGill UniversityCentre Hospitalier de l’Université de MontréalDouglas Mental Health University InstituteAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsMental healthPsychologyPsychiatry

Abstract

fetched live from OpenAlex

AIM: This paper outlines the transformation of youth mental health services in Edmonton, Alberta, a large city in Western Canada. We describe the processes and challenges involved in restructuring how services and care are delivered to youth (11-25 years old) with mental health needs based on the objectives of the pan-Canadian ACCESS Open Minds network. METHODS: We provide a narrative review of how youth mental health services have developed since our engagement with the ACCESS Open Minds initiative, based on its five central objectives of early identification, rapid access, appropriate care, continuity of care, and youth and family engagement. RESULTS: Building on an initial community mapping exercise, a service network has been developed; teams that were previously age-oriented have been integrated together to seamlessly cover the age 11 to 25 range; early identification has thus far focused on high-school populations; and an actual drop-in space facilitates rapid access and linkages to appropriate care within the 30-day benchmark. CONCLUSIONS: Initial aspects of the transformation have relied on restructuring and partnerships that have generated early successes. However, further transformation over the longer term will depend on data demonstrating how this has impacted clinical outcomes and service utilization. Ultimately, sustainability in a large urban centre will likely involve scaling up to a network of similar services to cover the entire population of the city.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.381
Teacher spread0.359 · 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 teacher head, not a consensus.

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

Citations21
Published2019
Admission routes3
Has abstractyes

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