Perspectives on implementing Individual Placement and Support (IPS) within primary health care settings for adults living in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Individual placement and support (IPS) is an evidence-based practice (EBP) designed to help people with severe mental illness re-enter the labour market. Implementing an IPS program within a new context (e.g., primary health care setting) to support populations that are complex and multi-barriered presents a set of unique challenges and considerations. This paper provides community-based perspectives that identify implementation strengths and challenges and highlights potential strategies aimed at addressing emergent barriers. METHODS: A case study was conducted across three community health centres in British Columbia (BC), Canada, where a novel IPS program was embedded within primary care services. Data collection consisted of open-ended surveys and focus groups with service providers directly involved in program implementation and their associated clinical and managerial support teams (n = 15). Using the updated Consolidated Framework for Implementation Research (CFIR) as a guide, we performed deductive thematic analysis to identify key areas impacting IPS implementation. RESULTS: Integration with existing health care systems and primary health care teams and support from leadership across all levels were identified as both key facilitators and barriers to implementation. Facilitators and barriers were identified across all domains, with those within innovation and process most easily addressed. Four cross-cutting themes emerged for promoting more integrated and sustainable program implementation: investing in pre-implementation activities, supporting a dynamic and flexible program, building from community experiences, and developing a system for shared knowledge. CONCLUSIONS: Implementing an IPS program embedded within primary health care settings is complex and requires extensive planning and consultation with community-based service providers and decision-makers to achieve full integration. Future practice and policy decisions aimed at supporting employment and well-being should be made in collaboration with communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".