“I've actually surprised myself at what I can do”: Understanding the longer-term experiences of individual placement and support (IPS) embedded within primary healthcare
Bibliographic record
Abstract
Background Persons with persistent and multiple barriers (PPMB), including mental health and substance use (MHSU) and other disabilities, often experience inequitable challenges obtaining and sustaining competitive employment opportunities. Specialized employment services, such as Individual Placement and Support (IPS), embedded within primary healthcare settings is one approach being trialed to address these inequities. Little is known about the medium and longer-term experiences of PPMB enrolled in IPS programs integrated within primary healthcare. Methods We conducted a longitudinal qualitative study using semi-structured interviews with program clients. We analyzed interviews (12- and 24-months) sequentially and collectively using a reflexive thematic approach. We developed, defined, and named themes iteratively, using annotations throughout the process to record personal reflections and assumptions. Results Thirty-one participants were enrolled in the study, with 31 interviews conducted at 12 months and 20 interviews at 24 months after program enrollment. Four key themes and one subtheme were generated: (1) negative work experiences have lasting and cyclical impacts, (subtheme) positive work experiences help break negative employment cycles, (2) health remains a significant barrier across the employment journey, (3) social connection is a foundation for health and employment, and (4) growth takes time: progress through self-discovery and support. Conclusion Continuous access to integrated specialized health and employment services are necessary for PPMB to achieve sustainable progress towards health, recovery, and employment. As such, integrated and multi-sector programs need to receive sustained funding and cross-ministerial support to ensure equitable employment opportunities for those with MHSU, PPMB, and other disabilities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".