Relational Approaches to Recruitment During and After the Pandemic: Strategies for Community-Led Research Initiatives With Indigenous Communities in Southern Ontario
Bibliographic record
Abstract
Recruiting research participants is a vital part of health research, necessary to obtain data that can yield meaningful results. Recruiting research participants, however, can be challenging and time-consuming. The COVID-19 pandemic resulted in significant challenges to participant recruitment, as traditional methods relying on in-person interactions were not feasible with health restrictions. This was particularly challenging for community-led research with Indigenous communities, where relational approaches to recruitment are culturally appropriate and ethically necessary to build community trust. This paper describes the recruitment methods of three different Indigenous community-led studies carried out in Southern Ontario during the COVID-19 pandemic and emphasizes the necessity for flexibility and responsive recruitment strategies during this time. Despite health regulations disrupting in-person approaches to building relationships during the pandemic, our priority for a relational approach to recruitment was achieved through strong relationships with community partners and the use of technology. The examples and strategies provided here contribute to the developing body of literature describing the impacts of the COVID-19 pandemic on qualitative and community-led research, of which little is available concerning recruitment strategies. Our experience and learning will be valuable to novice researchers and those who are new to community-led and relational approaches to research.
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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 | 0.069 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".