Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform
Bibliographic record
Abstract
BACKGROUND: Treatment burden refers to the workload imposed by healthcare on patients, and the effect this has on quality of life. The Treatment Burden Questionnaire (TBQ) aims to assess treatment burden in different condition and treatment contexts. Here, we aimed to evaluate the validity and reliability of an English version of the TBQ, a scale that was originally developed in French. METHODS: The TBQ was translated into English by a forward-backward translation method. Wording and possible missing items were assessed during a pretest involving 200 patients with chronic conditions. Measurement properties of the instrument were assessed online with a patient network, using the PatientsLikeMe website. Dimensional structure of the questionnaire was assessed by factor analysis. Construct validity was assessed by associating TBQ global score wıth clinical variables, adherence to medication assessed by Morisky's Medication Adherence Scale (MMAS-8), quality of life (QOL) assessed by the PatientsLikeMe Quality of Life Scale (PLMQOL), and patients' confidence in their knowledge of their conditions and treatments. Reliability was determined by a test-retest method. RESULTS: In total, 610 patients with chronic conditions, mainly from the USA, UK, Canada, Australia, or New Zealand, completed the TBQ between September and October 2013. The English TBQ showed a unidimensional structure with Cronbach α of 0.90. The TBQ global score was negatively correlated with the PLMQOL score (rs = -0.50; p < 0.0001). Low rather than moderate or high adherence to medication was associated with high TBQ score (mean [SD] TBQ score 61.8 [30.5] vs. 37.7 [27.5]; P < 0.0001). The treatment burden was higher for patients who had insufficient knowledge compared with those who had sufficient knowledge about their treatments (mean ± SD TBQ score 62.3 ± 31.3 vs. 47.8 ± 30.4; P < 0.0001) and conditions (63.0 ± 31.6 vs. 49.3 ± 30.7; P < 0.0001). The intraclass correlation coefficient for the retest (n = 282) was 0.77 (95% CI 0.70 to 0.82). CONCLUSIONS: We found that the English TBQ is a reliable instrument in this population, and provide evidence supporting the construct validity for its use to assess treatment burden for patients with one or more chronic conditions in English-speaking countries.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".