I-CREAte: Engaging Families to Build Healthy Communities – the community speaks
Bibliographic record
Abstract
Context: Adverse Childhood Experiences and Adverse Community Environments in combination represent family and community level factors that limit children, families and communities from reaching their full potential. Despite these significant challenges, every community has examples of resilience. Objectives: To understand the factors that impact family and community resilience for families facing adversity and families’ ideas on how to improve their communities. Study Design and Analysis: Using community-based participatory action research methods, the I-CREAte (Innovations for Community Resilience, Equity and Advocacy) team conducted a multiple case study design exploring the lived experiences of nine families experiencing adversity, focusing on their perceived strengths, barriers, and proposed solutions to improving resilience for their families and communities. Directed content analysis was used to analyze each case independently. Subsequently, all cases were brought together in a multiple case study analysis. Setting and Population: This study took place in Kingston, Frontenac, Lennox and Addington counties in Ontario, Canada. Nine families (cases) were recruited who selfidentified as experiencing adversity. Family experiences of adversity ranged from exposure to racism, substance use, poverty, disability, migration, single parenthood, and exposure to different forms of interpersonal and community violence. Instrument: Visual timelines, semi structured interviews, and photovoice were included as data collection tools for each of the nine cases. Results: Through the use of thematic analysis, twelve themes were retained which significantly impacted families’ experience of resilience. These included: lack of community safety, impacts of COVID-19, Indigenous community needs and experiences, challenges to Integration for newcomers, anti-discrimination, family supports, rights-based approach, impacts of material deprivation, health system navigation challenges, healthy cities, social support networks, and family friendly substance use treatment needs. A lack of awareness and embodiment of rights, otherwise known as epistemic injustice, was noticeable in many families. Conclusions: The ways in which themes impacted individual families and intersected with each other present many opportunities for meaningful community intervention to improve the well-being of families.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".