I-CREAte: Engaging Families to Build Healthy Communities: Building a Community led action plan
Bibliographic record
Abstract
Context: Using community-based participatory methods, the I-CREAte (Innovations for Community Resilience, Equity and Advocacy) team conducted a multiple case study exploring the lived experiences of families experiencing adversity. Through thematic analysis, twelve themes were retained which significantly impacted families’ and communities’ experience of resilience. Phase two of this study sought to use these themes to develop a community action plan to inform future research, program and policy making. Objectives: (1) To empower community members and partners to identify solutions for challenges that limit family and community resilience. (2) To develop a community driven action plan providing information for community members, organizations, and different levels of government to support implementation of programs aligned with community identified priorities. Study Design and Analysis: Community engagement meetings were conducted using participatory activities: world cafe, group prioritization, and facilitated group reflection. Findings were compiled in a participatory qualitative data analysis process with the I-CREAte community research team. Results were validated with I-CREAte community advisory board. Setting and Population: Kingston, Frontenac, Lennox and Addington counties in Ontario, Canada. Eleven community engagement meetings with different stakeholders, including community members with different identities (racialized newcomers, people who were unhoused or used substances, youth, seniors, low income community) and with service providers from partner organizations (the municipality, organizations providing services to youth, to people using substances, etc) Results: Proposed solutions were organized around key themes (eg. housing, building community, substance use services) as well as with a lens to specific audiences (community members, service organizations, municipal government, other levels of government and policy makers). Findings were compiled into a searchable database, and different knowledge mobilizations tools were used to share findings with relevant audiences. Conclusions: Ensuring community, service providers, and government are involved at all stages of the research process dramatically improves the likelihood that these groups will be interested in the findings of the research and will be willing to consider uptake of recommendations and support of future projects to determine best practices to meeting community needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".