Guidelines for conducting partnered research in applied psychology: An illustration from disability research in employment contexts
Bibliographic record
Abstract
Abstract The partnered research method, used routinely in other fields, offers great potential to improve the quality and practical use of applied psychology research. Partnered research integrates the perspectives of researchers, knowledge users, people who have lived experience with the attributes being studied, and other stakeholders in all elements of the research process, from the creation and generation of research questions to the methods used, the data analyzed, and the dissemination, application, and implementation of research results. We explain the concept of partnered research and provide a step‐by‐step roadmap for applied psychology scholars interested in conducting partnered research. In doing so, we also address common challenges with this method and provide advice on how to overcome them. We embed our description of the partnered research approach primarily in the context of research on disabilities and work but also offer examples drawn from other areas of applied psychology.
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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 | 0.490 | 0.397 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".