Implementation of a supported employment and education program in Integrated Youth Service settings across Canada: A multi-site case study
Bibliographic record
Abstract
Background: Youth mental health in the aftermath of the COVID-19 pandemic has worsened due to prolonged isolation, reduced access to resources, and disruptions to education and employment. To enhance service delivery for this population and in light of a growing body of work supporting integrated approaches in Canada, the Lift/Futur en tête project implemented an evidence-based supported employment and education program, Individual Placement and Support (IPS), into pre-existing Integrated Youth Service (IYS) networks. The purpose of this study is to produce early findings of the project by documenting experiences of service providers. Methods: We conducted a multi-site case study to examine similarities and differences among 12 participating sites across Canada using data from presentations at the Lift/Futur en tête Summit held in Toronto in the spring of 2023. Twelve sites across five Canadian provinces were included. Participating organizations include AOM [the Acadian Peninsula, Alberta, centre local de services communautaires Dorval-Lachine-LaSalle (DLL)/Ouest de l’Ile de Montréal], RIPAJ: Réseau d’Intervention de Proximité Auprès des Jeunes de la rue (RIPAJ)], Foundry (Campbell River, Comox Valley, Kelowna, Penticton), and YWHO (Haliburton, Niagara Region, North Simcoe, Central Toronto). Diverse in size and geographic make-up, each site has unique features with tailored services to meet the needs of the communities they serve. This study was conducted as a follow-up to the Local Journeys presentations at the Lift/Futur en tête Summit in the spring of 2023. The Local Journeys presentations were a time for organizations to briefly (in roughly 10 minutes) introduce their IPS team; describe the context they serve; share challenges, success stories, and unique features of implementation; and describe what worked or did not work when implementing the IPS model in their community. Data were gathered from PowerPoints, including images and videos in which sites selected pertinent data to share. Detailed notes were taken by several members of the research team for the duration of each presentation. Notes were translated if necessary and amalgamated to ensure accuracy and completeness. To identify differences and similarities across sites, we followed standard procedure for multi-site case study analysis. We began with a within-case analysis, in which data from each site were examined in-depth one-by-one to become familiar with the features unique to that site and identify pertinent information as expressed by the service providers. We then conducted a cross-case search of patterns between sites to determine instances of overlap and ascertain key areas of need. Contextualized by community size, findings are reported in categories of barriers, facilitators, and innovation. Results: Findings identified notable barriers, facilitators, and innovation strategies, with commonalities categorized by community size (small, medium, large). Conclusions: Future policy in this area should consider community needs when implementing the IPS model in IYS networks. Attention should be given to identifying solutions to transportation barriers in rural regions, working with Indigenous community members to ensure elements of the IPS model are consistent with Indigenous values and principles, working with clients with complex needs with attention to the social determinants of health (SDoH), and implementing IPS in virtual settings.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".