Empirical evidence for a process-based model of health-related quality of life using network analysis
Bibliographic record
Abstract
Background Health-related quality of life (HRQL) is the perceived impact of a medical condition on one's overall wellbeing. While contemporary assessments are structured to evaluate an individual's HRQL state, we propose a complementary process-based model, which is defined as an appraisal that evolves over time as it reflects and informs a self-regulatory process of adapting to dynamic changes in bio-psycho-social life domains. In support of this approach, we developed a novel HRQL assessment tool called the EUROIA: EvalUation of goal-diRected activities to prOmote wellbeIng and heAlth, which uses self-report data to assess the frequency with which individuals engage in a sample of goal-directed activities in pursuit of living well. Methods We conducted a network analysis to evaluate the hypothesis that the EUROIA subscales would demonstrate a meaningful pattern of associations with an established HRQL measure and associated indices of psychosocial functioning and efficacy in self-managing a chronic medical condition. Results The EUROIA is associated with established indices of HRQL in a manner that is theoretically consistent with our process-based model. Stability coefficients (i.e., betweenness, closeness, and strength) of the analysis revealed high reliability for the network. Conclusion This analysis provides support for the validation of a process-based approach to HRQL assessment, which is represented, in part, by the EUROIA. A process-based approach complements and expands conventional measures of HRQL by focusing on how a patient's capacity to engage in goal-directed activities for living well is affected by their medical condition.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".