MétaCan
Menu
← Back to cohort
Record W4390942596 · doi:10.5334/ijic.icic23671

Collaborative Networks: a Pragmatic and Innovative Approach to Meet the Mental Health and Addictions Care Crisis

2023· article· en· W4390942596 on OpenAlexaffabout
Sarah Jarmain, Eric Wong, Judith Ann Francis, Lisa Vreugdenhil, Arlene G. MacDougall

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsSt Joseph's Health CareWestern UniversityThames Valley Children's Centre
Fundersnot available
KeywordsMental healthContext (archaeology)TelepsychiatryHealth careMedicineCollaborative CareNursingAddictionTelehealthTelemedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Summary: In this highly interactive workshop, participants will learn how a unique implementation of the University of Washington AIMS Centre Collaborative Care Model (CoCM) for Mental Health and Addictions, within an interdisciplinary primary care setting, can improve access, clinical outcomes, and patient satisfaction by leveraging a population health approach, care management, and digital health technologies through creation of a network across organizations using existing resources. Background and Context: Mental health and substance use disorders are the leading cause of years lost to disability globally (Whiteford, 2013) and have significantly increased due to the impacts of the COVID-19 pandemic. Within the London-Middlesex region we are in crisis with local emergency rooms and psychiatric inpatient programs that are chronically over-capacity. London Canada is a mid-sized city within a largely rural county. Hospital based mental health services are organized between two large academic hospitals with acute services at one and tertiary care at another. There is also a small regional hospital without dedicated psychiatric services. There are several community and mental health addiction services but limited access to community psychiatry. Primary care has experienced significant challenges accessing psychiatry with wait times often over a year, and a fragmented mental health system that they find hard to navigate, with limited communication and coordination. Collaborative or integrated care has a strong research base demonstrating better short- and long-term clinical outcomes, across a variety of settings, for primary care patients with depression and anxiety, and was seen as an opportunity to improve access and care. Audience: Patient/family caregivers, clinicians, health leaders, policy makers Workshop Outline (90 minutes) Implementation and Evaluation Model (30 minutes) This workshop will introduce our unique model of collaborative mental health care which incorporates traditional collaborative care elements, a population health focus grounded in measurement-based care, the use of care manager roles, and virtual care technologies. Our model builds a virtual team with linkages between acute and tertiary care psychiatry, primary care, and community mental health and addictions creating an integrated care delivery network, within existing resources. Our methodology incorporates social innovation, and human centred design (partnering with patients/family caregivers, clinicians, and health care leaders). We have developed a framework for sustainably capturing real-world evaluation outcomes (patient/provider/program/system outcomes) that will inform future spread and scale of the model. Engagement of Participants – Living Lab (45 minutes) + Take Aways (15 minutes) The workshop will function as a living lab for participants to contribute to the development of a value proposition canvas while discussing two patient journey maps - our current state of a fragmented, difficult-to-access mental health and addiction system, and our future state of a coordinated collaborative primary mental health care network embedded within a larger stepped and staged care model. The workshop will include discussion of how this model aligns with current mental health and addiction policy within Ontario and the Ontario Health Team strategy. Participants will be asked to identify how a collaborative network approach could be used to address a population health need within their own settings and context.

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)0.020
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.000
Science and technology studies0.0080.006
Scholarly communication0.0100.009
Open science0.0040.019
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0250.005

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 head0.017
GPT teacher head0.371
Teacher spread0.354 · 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 designNot applicable
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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Integrated Care→Same topicDigital Mental Health Interventions→French-language works237,207→