Identifying domains of health-related quality of life: the perspective of individuals with low back pain
Bibliographic record
Abstract
BACKGROUND: Identifying the most relevant HRQOL domains for LBP from the perspective of individuals with lived experience with LBP is necessary to prioritize domains that will be most informative for evaluating the impact of pain and interventions while overcoming the burden of using long-form assessment tools. This study aimed to identify which domains of HRQOL are most important from the perspective of individuals with chronic LBP. METHODS: Semi-structured interviews were conducted with 26 individuals with LBP. Participants first responded to questions related to the impact of their LBP on their HRQOL. Then, using a card sorting method, they were asked to select and indicate HRQOL domains that were most relevant to them from a list of 18 cards that represented different HRQOL domains. Participants were asked to explain the reasoning for their selection. RESULTS: Participants identified physical activity restriction (50%), severity of pain (31%), social activity restriction (23%), and work performance restriction (23%) as the most important domains. The most frequently selected HRQOL domains during card sorting were social function (69%), pain intensity (62%), physical function (58%), fatigue (58%), and pain interference (42%). CONCLUSION: The most important domains of HRQOL perceived by participants were pain intensity, social function, physical function, fatigue, and pain interference. Identifying these domains will inform clinical decision-making and guide treatment choices for health care providers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".