Health from the Grassroots, Listening to Mob
Bibliographic record
Abstract
Abstract Background: There is opportunity for Universities to actively engage with Aboriginal communities to participate, conduct, and ideally lead, responsible research that attends to community priorities and issues. The Health from the Grassroots (Grassroots) project seeks to address an ongoing mismatch between university-defined priorities and community-defined priorities in rural [***]. Grassroots, led by Aboriginal staff of the [***], is a community engagement project aimed at engaging Aboriginal communities in conversations to inform research priorities. This paper describes the project vision, implementation, and lessons learned in the first years. Approach: The Grassroots project was a true representation of collaborative research led by and for Aboriginal people. We designed and conducted a local survey and yarning sessions with community members and used this information to design a “rich picture” to report findings and engage in further conversation with communities about evolving health and research priorities. We identified strengths and challenges faced by communities and health services in the region. The Aboriginal research team centred community in decision-making for project design and direction. Lessons Learned: Challenges encountered included limited resources and devoted time for the research team as this project occurred alongside staffs’ substantive positions. Community members were highly engaged in the consultation process and the rich picture continues to be used to further conversations about research action. Conclusions: Deep-rooted relationships and identities as members of the Community in which we live, and work enabled meaningful consultation to inform research action. Research priorities identified through the Grassroots project have been integrated into the ongoing work of the [***].
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".