The fertility quality of life (FertiQoL) tool: development and general psychometric properties†
Bibliographic record
Abstract
BACKGROUND: To develop the first international instrument to measure fertility quality of life (FertiQoL) in men and women experiencing fertility problems, to evaluate the preliminary psychometric properties of this new tool and to translate FertiQoL into multiple languages. METHOD: We conducted a survey, both online and in fertility clinics in USA, Australia/New Zealand, Canada and UK. A total of 1414 people with fertility problems participated. The main outcome measure was the FertiQoL tool. RESULTS: FertiQoL consists of 36 items that assess core (24 items) and treatment-related quality of life (QoL) (10 items) and overall life and physical health (2 items). Cronbach reliability statistics for the Core and Treatment FertiQoL (and subscales) were satisfactory and in the range of 0.72 and 0.92. Sensitivity analyses showed that FertiQoL detected expected relations between QoL and gender, parity and support-seeking. FertiQoL was translated into 20 languages by the same translation team with each translation verified by local bilingual fertility experts. CONCLUSIONS: FertiQoL is a reliable measure of the impact of fertility problems and its treatment on QoL. Future research should establish its use in cross-cultural research and clinical work.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".