Assessing the quality of online resources for inclusive research methods: Insights from a 2024 rapid review
Bibliographic record
Abstract
New academic researchers need resources to effectively learn how to conduct inclusive research and meaningfully engage co-researchers with intellectual and developmental disabilities. This rapid review aims to address: What online resources are available in 2024 for new academic researchers seeking to learn about inclusive research methods and what is the quality of those resources based on a set of criteria for assessing quality in inclusive research? A search of the literature was conducted. In total, 11 resources were included. A checklist with 11 items was developed to assess the quality of these resources. Results suggest a range of checklist criteria met among the resources. Among the 11 checklist items, some were consistently met while others were frequently overlooked. This study brings to light a need for addressing how senior academic researchers make resources for complex research methods available to others. Additionally, it demonstrates the importance of developing comprehensive, self-paced training with implementation support.
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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 | 0.134 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".