Changes to the subscales of two vision-related quality of life questionnaires are proposed
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Psychometrically sound questionnaires for the assessment of vision-related quality of life (QOL) are scarce. Therefore, the objective was to further validate two vision-related QOL questionnaires in a Dutch population of visually impaired elderly. METHODS: A total of 329 visually impaired older persons referred to low vision services completed the low vision QOL (LVQOL) and Vision-Related Quality of Life Core Measure (VCM1) questionnaires at baseline, after 1-4 weeks (retest), and after 5 months. Confirmatory factor analyses were performed on baseline data. The smallest detectable change (SDC) was assessed, based on the standard error of measurement (SEM). Change scores between the baseline and 5 months follow-up data were related to a general transition question to assess the minimal important change (MIC). Furthermore, the MIC was related to the SDC, to examine whether the MICs were detectable beyond measurement error. RESULTS: The original factor structures could not be confirmed. After omitting items and remodeling, adequate fits were obtained. SDCs comprised at least one quarter of the scale for all scales and subscales on the individual level and exceeded the MICs on every occasion. CONCLUSION: We propose MICs of 5-10 points for the scales and subscales of the LVQOL and VCM1. The questionnaires are not useful in the follow-up of individual patients
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".