A review of community engagement in cancer control studies among Indigenous people of Australia, New Zealand, Canada and the USA
Bibliographic record
Abstract
This review aimed to address studies of cancer control in Indigenous populations, with a focus on: (1) the nature and extent of community engagement; and (2) the extent to which community engagement has facilitated successful outcomes. Articles addressing Indigenous cancer control using some degree of community engagement were identified by a search of the following electronic databases: MEDLINE (via Ovid and Pubmed), psycINFO, CINAHL and Google Scholar. Relevant studies were scored and analysed according to Green et al.'s guidelines for participatory research. Studies often engaged the community only minimally. Where studies resulted in successful outcomes, they tended to have included Indigenous community members in genuine research roles, from planning, to implementation, to presentation of results at conferences. Studies with positive health outcomes were often initiated by a combination of academic researchers and community members or organisations. This narrative review highlighted significant scope for improvement in community-based studies addressing Indigenous cancer control. Increased attention to the philosophical underpinnings of community engagement is required to ensure that the benefits of this approach are translated to achieve improved cancer control outcomes. An increased awareness of the benefits of community engagement may prove effective in conducting cancer control research that leads to improved outcomes in Indigenous communities.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".