Addressing Substance and Alcohol Use Disorders in the Canadian Panjabi Diaspora
Bibliographic record
Abstract
**What are the challenges faced when treating Substance and Alcohol Use Disorders in the Panjabi diaspora, and how can the integration of the Panjabi-Centred Design Framework address these challenges effectively? **Through a series of case studies and personal narratives, author Imroze Singh Deol delves into the culturally nuanced strategies that align with the Panjabi community’s ethos, highlighting the necessity of empathy, community support, and empowerment in the recovery journey. The narrative weaves together cultural insights and contemporary design thinking to propose a holistic recovery pathway that respects the individual’s cultural identity.Addressing Substance and Alcohol Use Disorders in the Canadian Panjabi Diaspora scrutinizes the roles of various institutions — healthcare, community, and faith-based — in creating a synergistic support network, emphasizing a collaborative approach that harnesses the collective strengths to aid recovery.Advocating for systemic change, and a more inclusive framework that challenges existing colonial legacies and barriers within healthcare, this book is ideal reading for students of Human-Centered Design, Medicine, Social Work, and Ethnic Studies, as well as healthcare professionals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".