Evidence-Based and Community Engaged Pandemic Responses for Calgary
Bibliographic record
Abstract
Pandemics unduly burden those who are already economically and socially disadvantaged by poverty, disability, marginalization, and other vulnerabilities. Global pandemics increase disparities experienced by society’s most vulnerable, as inadequacies in systems-level protections make services challenging to access during emergencies. Families are specifically at risk, especially if they experience economic and social disparity concurrently with the pandemic. This study focuses on examining the way COVID-19 has exacerbated challenges to evidence based practice (EBP) implementation for community organizations in Calgary. Here we describe circumstances that make families vulnerable, the community organizations that support these families during the pandemic, and challenges with implementing EBP. To better understand policy barriers and facilitators as they relate to EBP access throughout the pandemic in Calgary, we conducted focus groups using the Nominal Group Technique (NGT) with diverse individuals representing local community organizations. Participants articulated the structural disparities that impede access to community-based EBP during the pandemic, and recognized potential solutions. The major themes uncovered in the research, as they apply to barriers to evidence-based service provision, included reduced revenue streams, access to technology, and lack of collaborative communication within and between ministries, as well as sectors. Proposed solutions to these barriers included person-centred policy and program approaches and reciprocal partnerships.
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".