The Role of Response-Shift in Studies Assessing Quality of Life Outcomes Among Cancer Patients: A Systematic Review
Bibliographic record
Abstract
Objective: Response-shift has been cited as an important measurement consideration when assessing patient reported quality of life (QoL) outcomes over time among patients with severe chronic conditions. Here we report the results of a systematic review of response shift in studies assessing QoL among cancer patients. Methods: A systematic review using MEDLINE, EMBASE, and PsychINFO along with a manual search of the cited references of the articles selected, was conducted. A quality review was performed using STROBE criteria and reported according to PRISMA guidelines. Results: A systematic review of 1487 records published between 1887 and December 2018 revealed 104 potentially eligible studies, and 35 studies met inclusion criteria for content and quality. The most common cancer patient populations investigated in these studies were breast (18 studies), lung (14 studies), prostate (eight studies) and colorectal (eight studies). Response shift was identified among 34 of the 35 studies reviewed. Effect sizes were reported in 17 studies assessing QoL outcomes among cancer patients; 12 of which had negligible to small effect sizes, four reported medium effect sizes which were related to physical, global QoL, pain and social (role) functioning and one reported a large effect size (fatigue). The most prevalent method for assessing response shift was the then-test, which is prone to recall bias, followed by the pre-test and post-test method. Given the heterogeneity among the characteristics of the samples and designs reviewed, as well as the overall small to negligible effect sizes for the effects reported, conclusions stating that changes due to internal cognitive shifts in perceived QoL should account for changes observed in cancer patients’ QoL outcomes should be interpreted with caution. Conclusion: Further work is needed in this area of research. Future studies should control for patient characteristics, time elapsed between diagnosis and baseline assessment and evaluate their contribution to the presence of response shift. Time between assessments should include short and longer periods between assessments and evaluate whether the presence of response shift holds over time. Possible avenues for inquiry for future investigation are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.185 | 0.460 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".