The EnRiCH Community Resilience Framework for High-Risk Populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Resilience has been described in many ways and is inherently complex. In essence, it refers to the capacity to face and do well when adversity is encountered. There is a need for empirical research on community level initiatives designed to enhance resilience for high-risk groups as part of an upstream approach to disaster management. In this study, we address this issue, presenting the EnRiCH Community Resilience Framework for High-Risk Populations. METHODS: The framework presented in this paper is empirically-based, using qualitative data from focus groups conducted as part of an asset-mapping intervention in five communities in Canada, and builds on extant literature in the fields of disaster and emergency management, health promotion, and community development. RESULTS: Adaptive capacity is placed at the centre of the framework as a focal point, surrounded by four strategic areas for intervention (awareness/communication, asset/resource management, upstream-oriented leadership, and connectedness/engagement). Three drivers of adaptive capacity (empowerment, innovation, and collaboration) cross-cut the strategic areas and represent levers for action which can influence systems, people and institutions through expansion of asset literacy. Each component of the framework is embedded within the complexity and culture of a community. DISCUSSION: We present recommendations for how this framework can be used to guide the design of future resilience-oriented initiatives with particular emphasis on inclusive engagement across a range of functional capabilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it