Translation and Validation of the Farage Quality of Life (FQoL™) Instrument for Consumer Products into Traditional Chinese
Bibliographic record
Abstract
The Farage Quality of Life™ questionnaire (FQoL™) was developed specifically to assess the impact of consumer products. The objective of this investigation was to achieve a Chinese language instrument. The FQoL™ underwent a forward and backward translation, with cognitive testing by 13 subjects. Slight modifications were made to the instrument, and an implementation study was conducted with 800 participants having a mean (±SD) age of 34.22 (±9.28) years. The subjects were randomly assigned to use 1 of 4 ultra absorbency pad products for the length of one menstrual cycle. Three pads (coded N, S and C) were products currently available on the retail market, a fourth (coded M) was an experimental product improvement on Product N. Subjects were asked to complete the FQoL™ once before (T1) and once after (T2) the start of their period, and the Least Square (LS) Means were determined. Within group comparisons for each item and FQoL™ subscale were conducted by comparing the LS Means for T1 vs. T2. Participants using Product N showed the highest number of significant (p<0.05) changes (11 items), demonstrating these subjects felt worse about items mainly in the subdomains for Emotions, Personal Pleasure, and Physical State. Participants using Product C showed significant changes in 7 items mainly in the subdomains for Emotion and Physical State. Participants using Product S and the experimental Product M showed significant changes in only 4 and 3 individual items, respectively. These were not associated with any particular domain or subdomain. Between group comparisons were conducted by comparing the LS Means for the T2 responses for each group. The group using Product N had LS Mean responses that were significantly worse than the group using Product M for the Emotion, Personal Pleasure and Physical State subdomains, the Energy/Vitality domain, and 2 individual items. The Product S group was worse than the Product M group for 2 individual items. The Product C group was worse than the Product M group for the Personal Pleasure and Physical State subdomains and 5 individual items. We found that the Chinese language FQoL™ detected changes in HRQoL during menstruation compared with before menstruation. Further, the measure was able to detect differences among groups of subjects using different menstrual protection products.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".