Response shift in patient-reported outcomes: definition, theory, and a revised model
Bibliographic record
Abstract
PURPOSE: The extant response shift definitions and theoretical response shift models, while helpful, also introduce predicaments and theoretical debates continue. To address these predicaments and stimulate empirical research, we propose a more specific formal definition of response shift and a revised theoretical model. METHODS: This work is an international collaborative effort and involved a critical assessment of the literature. RESULTS: Three main predicaments were identified. First, the formal definitions of response shift need further specification and clarification. Second, previous models were focused on explaining change in the construct intended to be measured rather than explaining the construct at multiple time points and neglected the importance of using at least two time points to investigate response shift. Third, extant models do not explicitly distinguish the measure from the construct. Here we define response shift as an effect occurring whenever observed change (e.g., change in patient-reported outcome measures (PROM) scores) is not fully explained by target change (i.e., change in the construct intended to be measured). The revised model distinguishes the measure (e.g., PROM) from the underlying target construct (e.g., quality of life) at two time points. The major plausible paths are delineated, and the underlying assumptions of this model are explicated. CONCLUSION: It is our hope that this refined definition and model are useful in the further development of response shift theory. The model with its explicit list of assumptions and hypothesized relationships lends itself for critical, empirical examination. Future studies are needed to empirically test the assumptions and hypothesized relationships.
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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 | 0.089 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".