Exploring strategies on how the Government of Alberta could best engage community organizations to co-design pandemic-related policies and interventions for persons with disabilities.
Bibliographic record
Abstract
Persons with disabilities and their families are disproportionately impacted by the COVID-19 pandemic and the policy measures adopted in response. Given the increased risk for this vulnerable population group, the governments must engage stakeholders like community organizations and families of persons with disabilities to plan and co-design pandemic response plans. Harnessing experiential knowledge of and fostering collaboration with such stakeholders could aim in transforming services in crucial areas like health, where emergency policies and programs are organized around the needs of persons with disabilities. Initiatives that enhance civil society participation in designing policies and programs create a more transparent and responsive government and improve government programs' buy-in. Unfortunately, there is inadequate data collection and insufficient emergency preparedness planning and response for people with disabilities. The goal of this research was to identify the critical barriers from the government perspective while exploring best engagement strategies for co-designing rapid policy responses during the COVID-19 outbreak in Alberta. Twelve qualitative interviews with key decision-makers from provincial, municipal governments and disability advisory groups’ members were conducted. Using semi-structured interviews, the participants were asked open-ended questions on key thematic areas to address the research objective. Key findings from the research highlighted the participants’ viewpoints on barriers, aspects and preferences, which are the critical approaches through which the Government of Alberta engages with community organizations. First, health emergency policy responses need to view disability and poverty as interconnected factors to improve the overall quality of life for persons with disabilities. Findings also highlighted that top-down and tokenistic consultation approaches further limit the disability community's engagement in co-designing pandemic planning and response. Furthermore, inaccessible ways of consultation and navigation barriers exacerbate the impediments to a holistic co-design process. Findings also revealed that communication of pandemic information in accessible formats and tools is the most preferred co-design aspect to engage community organizations in developing a pandemic response. On the other hand, stakeholders' engagement in the government’s data surveillance efforts was unclear, and the overarching process of impact assessment needs to be strengthened. The research found that the COVID-19 disability group and the advisory council's presence at the federal and provincial levels is a robust mechanism that connects communities with the government. However, the process of influencing government decision making and policy actions needs to be openly communicated. The research recommends that governments transition from traditional consultative approaches to innovative engagement practices while sharing information on how public policies reflect communities’ input. Decision-makers are further recommended to make financial investments to include priorities of persons with disabilities in the pandemic planning and response. The decision-makers should also formally engage stakeholders like community organizations to co-design communication aspects of the pandemic response plan, along with the monitoring and evaluation of the plan. Medical or cultural barriers pose challenges for persons with disabilities to participate in co-designing policies. Further research is required to explore how families of persons with disabilities could be engaged to co-design public policies and interventions.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".