Is it worth the weight? : revisiting weighted and unweighted scores with a quality of life measure
Bibliographic record
Abstract
Subjective assessments of importance have been used as a weighting factor in measurement in a number of areas of research including quality of life, self esteem and job satisfaction. Despite the powerful intuitive appeal of this practice, conceptual and psychometric concerns with importance weighting have been raised, and research using weighted scores has produced mixed results. The advantages of importance weighting have therefore not been clearly established. The present study revisits importance weighting using data collected with the Injection Drug User Quality of Life Scale (IDUQOL). Weighted and unweighted IDUQOL scores from a subset of 241 participants from the Vancouver Injection Drug User Study (VIDUS) were correlated with measures of convergent and discriminant validity and a large number of criterion variables including drug use, stability of housing, involvement in drug treatment, and hospitalization. The contribution of importance ratings to scores on a global measure of life satisfaction was calculated using regression analysis. To determine whether importance ratings contributed significantly to the weighted IDUQOL total scores, analysis of variance was employed. Overall results of these analyses suggest that incorporating importance does not enhance the measurement of quality of life for this sample. However, the mean of satisfaction ratings for all important domains correlated significantly higher than the mean of satisfaction ratings for all unimportant domains with measures of convergent validity. It appears that the impact of importance depends at least in part on how it is measured and used. Further research may uncover methods for incorporating subjective importance that do increase the sensitivity of the IDUQOL and other quality of life measures.
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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 | 0.054 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".