I-CREAte: Engaging Families to Build Healthy Communities – a Photovoice presentation
Bibliographic record
Abstract
Context: During the pandemic, the stress of COVID-19 combined with pre-existing adversity put significant strain on the resilience of families and communities exposed to adverse childhood experiences and/or adverse community environments. I-CREAte (Innovations for Community Resilience, Equity and Advocacy) is a community based participatory action research program whose focus is to explore, advocate, and act on initiatives to enhance family and community equity and resilience. Objectives: To explore solutions identified by families experiencing adversity on how to enhance resilience and to improve the lives of other families in the communities in which they live. Study Design and Analysis: Part of a larger multiple case study entitled “Engaging Families to Build Healthy Communities” this study used photovoice to explore families’ perceptions of their own resilience by asking them to take photos that illustrated the strengths and weaknesses in their family and community, and to use these photos to reflect on solutions to improve family resilience in their community. Data was analyzed using thematic analysis, and participants were supported to provide a narrative highlighting important components of their photographs. Findings were disseminated using an arts-based montage in multiple settings. Setting and Population: This study took place in Kingston, Frontenac, Lennox and Addington counties in Ontario, Canada. Nine families were recruited who selfidentified as experiencing different forms of adversity. Instrument: Tablets were provided to families to capture photographs, followed by semi-structured interviews. Results: Families identified many sources of strength, including municipal services (libraries, community centers), formal and informal support networks, access to nature, and personal and intrafamilial characteristics, which had all contributed to their resilience in the face of significant adversity. Families articulated ways in which their communities could enhance the resilience of others through policy-level approaches, as well as community based mutual aid activities. Conclusions: The photovoice approach puts the narrative in the hands of the participant as the story-teller of their own experience, enabling the voices of some traditionally marginalized families to be heard throughout the community to inform program and policy makers as well as peers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".