Quality of life assessment instruments for adults: a systematic review of population-based studies
Bibliographic record
Abstract
BACKGROUND: Against a backdrop of population aging and improving survival rates for chronic noncommunicable diseases (CNCD), researchers are placing growing emphasis on health-related quality of life (HRQoL). The aim of this study was to identify the QoL assessment instruments used in population-based studies with adults conducted around the world. METHODS: A systematic review of original research published in all languages between 2008 and 2018 was conducted. Systematic reviews and meta-analyses were excluded. RESULTS: Sixty-three articles (38.1% conducted in the Americas) fitted the eligibility criteria. Based on the AHRQ checklist for cross-sectional studies and the Newcastle-Ottawa scale for cohort studies, methodological quality was shown to be fair in the majority of studies (55.6%) and good in 44.4%. The country with the highest number of publications was Brazil (20.6%). Twelve types of generic instruments and 11 specific instruments were identified. The generic instrument SF-36 was the most frequently used measure (33.3% of studies). In-home interviewing was exclusively used by 47.6% of the studies, while 39 studies (61.9%) reported the use of self-administered questionnaires. Over two-thirds of the studies (34.9%) used questionnaires to investigate the association between chronic diseases and/or associated factors. CONCLUSIONS: It was concluded that the wide range of instruments and modes of questionnaire administration used by the studies may hinder comparisons between population groups with the same characteristics or needs. There is a lack of research on QoL and the factors affecting productive capacity. Studies of QoL in older persons should focus not only on the effects of disease and treatment, but also on the determinants of active aging and actions designed to promote it. Further research is recommended to determine which QoL instruments are best suited for population-based studies.
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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 | 0.035 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".