MétaCan
Menu
Back to cohort
Record W4413358719 · doi:10.5334/ijic.nacic24090

Implementing a learning health system within an integrated youth service initiative -a real-world case study.

2025· article· en· W4413358719 on OpenAlexaboutno aff
Skye Barbic, Christine Mulligan, Anna-Joy Ong, Kelli Wuerth

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careService (business)Process managementHealth servicesHealth careKnowledge managementBusinessNursingMedicineComputer sciencePopulationPolitical scienceMarketing

Abstract

fetched live from OpenAlex

Background: Mental health and substance use disorders among youth continue to be of critical public health concern in Canada, and urgent action is required within the health system to better respond to the evolving health and wellness needs of youth. Foundry, an integrated youth services (IYS) initiative in British Columbia (BC), is piloting a learning health system (LHS) framework at two (out of 7) operational IYS centres. The goal of Foundry LHS is to optimize the flow of data to knowledge to practice, supporting rapid adaptation and the continuous improvement of service delivery, experiences, and wellness outcomes for youth. The objective of this study was to elucidate learnings from the pilot experience and identify the barriers and facilitators to LHS implementation in an IYS context. Approach: This study was co-designed with the IYS initiative and incorporated an integrated knowledge translation approach. Participants (n=2) were purposively recruited from the LHS implementation team and Foundry Central Office (i.e., the backbone organization supporting the community-based IYS centres). Individual interviews were conducted at the pre- and post-implementation stages of the pilot LHS project and followed a semi-structured guide to broadly capture knowledge and perceptions of the LHS framework, barrier and facilitators to its implementation, and the potential impact on the IYS and its community. Data were analyzed using directed content analysis, guided by the Theoretical Domains Framework and COM-B model. Overarching themes and subthemes were generated, and findings were further interpreted to determine the barriers and facilitators to implementing the LHS in an IYS context, as well as other key learnings from the pilot implementation process. Results: This real-world case study highlights the importance of ensuring that LHS values and structures are incorporated into the overall organizational culture of an IYS initiative. Key themes in participant perception of barriers to the pilot implementation included a lack of clarity, shared vision, change management, and meaningful engagement, leading to limited buy-in and motivation for change. Strong governance, leadership, operational support, and dedicated resourcing over time emerged as strong themes critical to the successful implementation of the LHS in an IYS context. Participants saw the pilot LHS project as an invaluable learning experience and perceived early barriers to be future facilitators of the LHS in this context. Importantly, participants felt that the IYS organizational values, people, and infrastructures were highly aligned with an LHS way of working. These ideas reflected major facilitators and were thought to have created optimism and motivation to continue the work of implementing an LHS across the initiative. Implications: Learnings from this project reveal important challenges and opportunities related to the implementation of an LHS within a dynamic and evolving IYS initiative. Results will be integral to informing the continued development, implementation, and scale-up of the LHS within the IYS context in BC and beyond.

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)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0130.005
Scholarly communication0.0050.003
Open science0.0030.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.473
Teacher spread0.418 · 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 designCase report
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Integrated CareSame topicInterprofessional Education and CollaborationFrench-language works237,207