Needs and readiness assessments: Tools for promoting community-university engagement with Aboriginal communities
Bibliographic record
Abstract
Community-based participatory research (CBPR) is an important means of connecting the perspectives of community members with critical social issues, such as health and wellness. As beneficial as CBPR can be, effective engagement with community members remains a difficult goal to achieve. In this article, we draw on the international literature around needs and readiness assessments to explore their potential for establishing solid foundations for engaged research. We examine the stages and dimensions identified in the literature, and use these as a framework for a needs and readiness assessment project undertaken with a Métis Settlement community in Alberta, Canada. We share how the needs and readiness assessments helped to foster the emergence of community priorities, informing the next steps in research design, program content and evaluation methods, and heightening community-university engagement. It is our hope that our example of engagement, which focuses on the role of needs and readiness assessments in strengthening community-university partnerships, will better inform engagement approaches so that they become relevant, culturally appropriate and community specific. Keywords: Métis, Aboriginal, community-based participatory research, needs assessment, readiness assessment, community-university partnership
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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 | 0.048 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".