Psychometric Properties of Quality of Life Assessment Tools in Morbid Obesity: A Review of Literature
Bibliographic record
Abstract
Background: Since studies have demonstrated that morbid obesity can exponentially impair quality of life, the measurement of quality of life is paramount to monitoring the effects of treatment and influences the development of clinical pathways, service provision, healthcare expenditures, and public health policy. Accordingly, clinicians, researchers, and policy makers must rely on valid instruments. Aim: This study aimed to review and critique the psychometric properties of some specific tools by COSMIN checklist and their application among morbidly obese individuals. Method: We searched PubMed, Web of Science, PsycINFO, Ovid, Elsevier, and ScienceDirect by using the keywords related to the Quality of Life Questionnaire, namely “morbid obesity”, “tool”, and “scale”, to retrieve articles published during 1989-2017. Then, the psychometric properties of the selected tools were assessed using the COSMIN checklist. Results: Most of the tools had not reported complete and desirable psychometrics properties. Demonstration of responsiveness from independent randomized controlled trials was not available in two of the eight questionnaires. These tools also did not report proper definition of interpretability. However, the data obtained by COSMIN checklist showed that Laval questionnaire is a proper scale for measuring quality of life in obese individuals, which can be recommended to researchers. Implications for Practice: Although Laval questionnaire was found a proper tool for measuring the quality of life among morbid obese patients, developing an instrument suitable for different societies with varied cultural and social characteristics is suggested because socio-cultural factors can influence the quality of life.
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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 | 0.032 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".