I-CREAte: Engaging Families to Build Healthy Communities: Inclusion as key to enhance resilience and community strength
Bibliographic record
Abstract
Context: Many families in our communities face forms of adversity including experiences of discrimination, exclusion and marginalization. Social exclusion can be related to socially stigmatized behaviors (substance use, mental health conditions, etc), racism, ableism, classism, and discriminatory attitudes and behaviors against linguistic or other minority groups. I-CREAte (Innovations for Community Resilience, Equity and Advocacy) is a community based participatory action research program whose aim is to explore, advocate, and act on initiatives to enhance family and community equity and resilience. Objectives: A sub-analysis of a broader study exploring the barriers and facilitators to family resilience during the COVID-19 pandemic, this study aims to explore the construct of inclusion or exclusion as experienced by families facing adversity. Study Design and Analysis: Mixed methods, multiple case study of nine families. Each of nine “cases” (families) was initially analyzed independently using directed content analysis. All nine cases were brought together in a participatory multiple case study analysis. Finally, after identification of inclusion as a key theme, all data was analyzed a third time using thematic analysis to explore themes related to inclusion and exclusion. Setting and Population: This study took place in Kingston, Frontenac, Lennox and Addington county in Ontario, Canada. Nine families self-identified as experiencing different forms of adversity were recruited, including newcomers to Kingston (internal migrants within Canada as well as new immigrants), racialized families, families with disabilities, linguistic minorities, and families experiencing other forms of social exclusion. Instrument: Data collection tools include visual timelines, semi structured interviews, and photovoice. Results: Preliminary analysis suggests that an experience of inclusion or exclusion is fundamental to families’ experience of resilience and wellbeing. Inclusion can be practiced informally, through engagement between community members at parks and in public spaces, or formally, through institutions such as places of worship, healthcare services and other publicly funded programs. Participants identified a need for increased awareness and training for community members and service providers. Conclusion: Newcomers to the community would benefit from programs that are accessible and explicitly designed to help them integrate into the community.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".