Equity and inclusivity in research: co-creation of a digital platform with representatives of marginalized populations to enhance the involvement in research of people with limited literacy skills
Bibliographic record
Abstract
To improve health equity, as well as equity in research, community-engaged research and participatory research needs to be inclusive. Equity in health research refers to the principle that anyone affected by research or who can benefit from its outcomes should have equal opportunities to contribute to it. Many researchers advocate the importance of promoting equity in research and engage in processes that foster the research involvement of lay persons, patients, and community members who are otherwise "absent" or "silent". Still, people with limited literacy skills who experience unwarranted structural barriers to healthcare access have little involvement in research. Low literacy is a major barrier to equity in health research. Yet there exist approaches and methods that promote the engagement in research of people with literacy challenges. Building on our previous research projects conducted with community members using participatory visual and sound methods (participatory mapping, photovoice, digital storytelling, etc.), we embarked on the co-creation of a digital platform in 2017. Our aim in this commentary is to report on this co-creation experience that was based on a social justice-oriented partnership. The development of the online platform was overseen by a steering committee made up of workers from community organizations involved with people with limited literacy skills, students, and researchers. In the development process, the co-creation steps included a literature review, informal interviews with key informants, and discussion and writing sessions about format and content. After numerous challenges raised and addressed during co-creation, the Engage digital platform for engagement in research went live in the winter of 2020. This platform presents, on an equal footing, approaches and methods from academic research as well as from the literacy education community engaged with people with limited literacy skills.
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 | 0.132 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.070 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".