Gaawaadhi Gadudha: understanding how cultural camps impact health, well-being and resilience among Aboriginal adults in New South Wales, Australia—a collaborative study protocol
Bibliographic record
Abstract
Introduction The health and well-being of Aboriginal Australians is inextricably linked to culture and Country. Our study challenges deficit approaches to health inequities by seeking to examine how cultural connection, practice and resilience among Aboriginal peoples through participation in ‘cultural camps’ held on sites of cultural significance promotes health and well-being. Methods and analysis The study will be undertaken in close collaboration and under the governance of traditional cultural knowledge holders from Yuwaalaraay, Gamilaraay and Yuin nation groups in New South Wales, Australia. Three cultural camps will be facilitated, where participants (n=105) will engage in activities that foster a connection to culture and cultural landscapes. A survey assessing connection to culture, access to cultural resources, resilience, self-rated health and quality of life will be administered to participants pre-camp and post-camp participation, and to a comparative group of Aboriginal adults who do not attend the camp (n=105). Twenty participants at each camp (n=60) will be invited to participate in a yarning circle to explore cultural health, well-being and resilience. Quantitative analysis will use independent samples’ t-tests or χ2analyses to compare camp and non-camp groups, and linear regression models to determine the impact of camp attendance. Qualitative analysis will apply inductive coding to data, which will be used to identify connections between coded concepts across the whole data set, and explore phenomenological aspects. Results will be used to collaboratively develop a ‘Model of Cultural Health’ that will be refined through a Delphi process with experts, stakeholders and policymakers. Ethics and dissemination The study has ethics approval from the Aboriginal Health and Medical Research Council (#1851/21). Findings will be disseminated through a combination of peer-reviewed articles, media communication, policy briefs, presentations and summary documents to stakeholders.
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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".