An Umbrella Review of Reporting Quality in CHI Systematic Reviews: Guiding Questions and Best Practices for HCI
Bibliographic record
Abstract
Systematic reviews (SRs) are vital to gathering and structuring knowledge, yet descriptions of their procedures are often inadequate. In human–computer interaction (HCI), SRs are still uncommon but gaining momentum, which prompted us to explore how SRs are reported at CHI—the flagship HCI conference venue. To assess the reporting quality of CHI reviews that aim for a systematic approach, we conducted an umbrella review and applied reporting guidelines for SRs (PRISMA and ENTREQ) to our corpus. We contribute the first exploration of how well SRs at CHI meet guidelines for reporting quality, showcasing strategies for improvement in reporting and conducting SRs especially in the domains of appraisal, synthesis, and documentation (i.e., protocol development). Finally, we present guiding questions for HCI researchers and practitioners for reporting SRs, as well as suggestions for best practices.
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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 | 0.813 | 0.903 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.059 | 0.047 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.013 | 0.027 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".