Response shift results of quantitative research using patient-reported outcome measures: a descriptive systematic review
Bibliographic record
Abstract
PURPOSE: The objective of this systematic review was to describe the prevalence and magnitude of response shift effects, for different response shift methods, populations, study designs, and patient-reported outcome measures (PROM)s. METHODS: A literature search was performed in MEDLINE, PSYCINFO, CINAHL, EMBASE, Social Science Citation Index, and Dissertations & Theses Global to identify longitudinal quantitative studies that examined response shift using PROMs, published before 2021. The magnitude of each response shift effect (effect sizes, R-squared or percentage of respondents with response shift) was ascertained based on reported statistical information or as stated in the manuscript. Prevalence and magnitudes of response shift effects were summarized at two levels of analysis (study and effect levels), for recalibration and reprioritization/reconceptualization separately, and for different response shift methods, and population, study design, and PROM characteristics. Analyses were conducted twice: (a) including all studies and samples, and (b) including only unrelated studies and independent samples. RESULTS: Of the 150 included studies, 130 (86.7%) detected response shift effects. Of the 4868 effects investigated, 793 (16.3%) revealed response shift. Effect sizes could be determined for 105 (70.0%) of the studies for a total of 1130 effects, of which 537 (47.5%) resulted in detection of response shift. Whereas effect sizes varied widely, most median recalibration effect sizes (Cohen's d) were between 0.20 and 0.30 and median reprioritization/reconceptualization effect sizes rarely exceeded 0.15, across the characteristics. Similar results were obtained from unrelated studies. CONCLUSION: The results draw attention to the need to focus on understanding variability in response shift results: Who experience response shifts, to what extent, and under which circumstances?
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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 | 0.166 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".