Measuring health-related quality of life in adults with chronic conditions in primary care settings Critical review of concepts and 3 tools
Bibliographic record
Abstract
Abstract Objective To describe health-related quality of life (HRQOL) conceptual frameworks, critically review 3 commonly used HRQOL scales relevant to adults with chronic conditions in primary care settings, and make recommendations for using HRQOL scales in primary care practice. Data sources Information was accessed regarding HRQOL conceptual and theoretical approaches. A comprehensive search strategy identified 3 commonly used scales that met the review criteria and evidence regarding use of the scales in adults with chronic conditions in community settings. Scale selection Scales were selected if they were designed for clinical use; were easy to administer; were generic and broad in content areas; and contained some individualized items. Scales were critiqued according to content development, theoretical basis, psychometric properties, scoring, feasibility, the concepts being measured, and the number of items that measured an individualized concept. Synthesis Early HRQOL approaches focused on health and functional status while recent approaches incorporate individualized concepts such as the person’s own values and the environment. The abbreviated World Health Organization Quality of Life Scale (WHOQOL-BREF), the 36-Item Short Form Health Survey (SF-36), and the Duke Health Profile were critiqued. All address physical, mental, and social domains, while the WHOQOL-BREF also addresses environment. Psychometric evidence supports use of the SF-36 and WHOQOL-BREF with this population. The SF-36 has the most evidence of responsiveness but has some floor and ceiling effects, while the WHOQOL-BREF does not appear to have floor or ceiling effects but has limited evidence of responsiveness. The WHOQOL-BREF has the highest proportion of individualized items. Conclusion Measurement of HRQOL in adults with chronic conditions can support patient management and contribute to primary care service evaluation. Scales that are based on a broad definition of health and that address the individualized nature of HRQOL are appropriate for these purposes, such as the WHOQOL-BREF. Psychometric evidence supports using this scale for adults with chronic conditions; more information about its responsiveness is needed.
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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 | 0.047 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".