Response shift in results of patient-reported outcome measures: a commentary to The Response Shift—in Sync Working Group initiative
Bibliographic record
Abstract
PURPOSE: The Working Group undertook a critical, comprehensive synthesis of the response shift work to date. We aimed to (1) describe the rationale for this initiative; (2) outline how the Working Group operated; (3) summarize the papers that comprise this initiative; and (4) discuss the way forward. METHODS: Four interdisciplinary teams, consisting of response shift experts, external experts, and new investigators, prepared papers on (1) definitions and theoretical underpinnings, (2) operationalizations and response shift methods, (3) implications for healthcare decision-making, and (4) on the published magnitudes of response shift effects. Draft documents were discussed during a two-day meeting. Papers were reviewed by all members. RESULTS: Vanier and colleagues revised the formal definition and theory of response shift, and applied these in an amended, explanatory model of response shift. Sébille and colleagues conducted a critical examination of eleven response shift methods and concluded that for each method extra steps are required to make the response shift interpretation plausible. Sawatzky and colleagues created a framework for considering the impact of response shift on healthcare decision-making at the level of the individual patient (micro), the organization (meso), and policy (macro). Sajobi and colleagues are conducting a meta-analysis of published response shift effects. Preliminary findings indicate that the mean effect sizes are often small and variable across studies that measure different outcomes and use different methods. CONCLUSION: Future response shift research will benefit from collaboration among diverse people, formulating alternative hypotheses of response shift, and conducting the most conclusive studies aimed at testing these (falsification).
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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 | 0.653 | 0.862 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.025 | 0.019 |
| Research integrity | 0.076 | 0.115 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".