Report: Rural Resilience and Community Connections in Health: Outcomes of a Community Workshop
Bibliographic record
Abstract
Canadians living in rural communities are diverse, with individual communities defined by unique strengths and challenges that impact their health needs. Understanding rural health needs is a complex undertaking, with many challenges pertaining to engagement, research, and policy development. In order to address these challenges, it is imperative to understand the unique characteristics of rural communities as well as to ensure that the voices of rural and remote communities are prioritized in the development and implementation of rural health research programs and policy. Effective community engagement is essential in order to establish rural-normative programs and policies to improve the health of individuals living in rural, remote, and northern communities. This report was informed by a community engagement workshop held in Golden Lake, Ontario in October 2019. Workshop attendees were comprised of residents from communities within the Madawaska Valley, community health care professionals, students and researchers from Carleton University in Ottawa, Ontario, and international researchers from Australia, Sweden, and Austria. The themes identified throughout the workshop included community strengths and initiatives that are working well, challenges and concerns faced by the community in the context of health, and suggestions to build on strengths and address challenges to improve the health of residents in the Madawaska Valley.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".