Cross-cultural Translation of the Adolescent Menstrual Bleeding Questionnaire (AMBQ)
Bibliographic record
Abstract
Abstract Background Heavy menstrual bleeding (HMB) affects up to 37% of adolescents. Many aspects of their lives are affected by HMB, including school attendance and participation in sports and social activities, underscoring the importance of evaluating patient reported outcomes in addition to physical outcomes in the assessment of HMB. Given the paucity of available tools to assess health-related quality of life (HRQoL) in adolescents with HMB, we developed the Adolescent Menstrual Bleeding Questionnaire (aMBQ), a valid and reliable measure of bleeding-related quality-of-life. The aim of this study was cross-cultural translation and adaptation of the English aMBQ into French to ensure accessibility for all Canadian adolescents who menstruate. Methods A 5-step process was followed: 1) forward translation of English aMBQ to Canadian French; 2) backward translation of aMBQ in French to English by a professional translation service; 3) review of the source and translated aMBQ to create a reconciled version; 4) cognitive debriefing to ensure linguistic, cultural, and clinical equivalence, and 5) review of cognitive debriefings to determine if changes were required and to produce the final version of the French aMBQ. This process identified words, concepts, and response options which are not clear. Results of cognitive debriefings were reviewed after every 3 participants; items were revised if presented as an issue by ≥ 2 participants. These changes were implemented and tested in cognitive debriefings until saturation was reached. Results Lingustic changes were made to 9 (33%) of the questions and one (3.7%) answer options. Major changes were made to 4 of the 27 questions (15%), and minor changes were made to 5 of the 27 questions (19%). One instruction item has changed, and multiple items were bolded for attention to specific words. Conclusions Professional translators, clinical experts, and patient input through cognitive debriefing are pivotal to successful cross-cultural translation. Results of cognitive debriefing interviews suggest the French aMBQ is easily understood and confirms its face validity. The French aMBQ will be made available on the mobile health application, WeThrive, in the near future.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".