Co-creating occupational science research with 2SLGBTQIA+ Indigenous communities: Developing community-driven research priorities through relationship-building
Bibliographic record
Abstract
Occupation-based research continues to expand the methods and approaches through which occupational experiences are explored. However, research agendas often overlook input from the community of interest when it comes to establishing the research priorities. With a history rooted in colonialism and erasure of Indigenous Peoples and their experiences, occupational science research carried out with 2SLGBTQIA+ Indigenous Peoples requires intentional and occupation-focused priority setting in order to develop and implement studies that align with a community’s needs, interests, and values. The priority setting project described in this methodological paper centres the intersection of occupation, as a means of facilitating a research-focused relationship, with the process of fostering community involvement. In alignment with the principles of a community-based research approach, the partnerships created throughout this work have been instrumental to the implementation and success of the project described. We utilized occupations both as a means of engagement for developing the priorities of a larger research study (e.g., doing a structured activity together while discussing the research), and as an approach to building and maintaining relationships (e.g., tea, visits, shared meals, walks in nature). Through an intentional coming together of a community of 2SLGBTQIA+ Indigenous Peoples, there was collective engagement in occupations that facilitated co-creation of a research study designed with, by, and for community. Occupational science research holds much potential when it comes to better understanding the diverse ways in which people experience their occupations, as evidenced in the process and learnings of the collaboration described in this paper.
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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 | 0.071 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".