QIM19-119: Validity of the French-Language Mammography Satisfaction Instrument Evaluating Women’s Satisfaction With an Organized Breast Cancer Screening Program: A Confirmatory Study
Bibliographic record
Abstract
Background: To evaluate the quality of an organized mammography screening program based on the perception of screened women, we developed and validated the French-language Mammography Satisfaction Instrument (MSI). The study objective was to confirm the validity and reliability of the MSI. Methods: A confirmatory study was conducted among 529 women who had had a recent screening mammography under the Quebec Breast Cancer Screening Program (PQDCS). Eligible women from the Quebec City region completed the online MSI between January 14 and May 23, 2016. The MSI originally included 14 items evaluating 4 factors: satisfaction with (1) the technician’s skills; (2) the physical environment; (3) the staff’s communication skills; and (4) the information provided under the program. A fifth factor with 2 items, evaluating mammography accessibility, was added. A confirmatory factor analysis (CFA) was done and goodness of fit indices were generated (SRMR, CFI, RMSEA with its 90% CI). Item reliability and composite reliability were estimated. Variance extract estimates (VEEs) were generated to assess the amount of variance explained by the factors. Multivariate logistic regressions were done to test the sensitivity of each scale to identify subgroups of unsatisfied women. Odds ratios (OR) and their 95% CI were estimated. Results: Most women (62.0%) were aged 55–64, and 32.9% had university level education. The CFA demonstrated that the 5 factors fitted the data well (SRMR=0.044; CFI=0.969; RMSEA=0.055 with its 90% CI: 0.047–0.063). Item reliabilities (≥0.55) and composite reliabilities (≥0.87) were high. All VEEs were also high (≥0.70). Pain during compression, anxiety before the mammography, perception of not having an excellent health, and the radiologic centers were the factors the most consistently and significantly associated with the 5 scales of satisfaction. In addition, women with university level education were less satisfied with the staff’s communication skills (OR=0.64; 95% CI: 0.43–0.97) and those having had less than 10 lifetime mammograms were less satisfied with the physical environment (OR=0.52; 95% CI: 0.33–0.83) and the accessibility (OR=0.53; 95% CI: 0.31–0.92). Conclusions : This confirmatory study showed the good reliability and validity of the construct of the 16-item MSI with 5 factors. The MSI is useful to detect unsatisfied women and to improve the quality of organized breast cancer screening programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".