Development and validation of the McGill body image concerns scale for use in head and neck oncology (MBIS‐HNC): A mixed‐methods approach
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and validate a patient-reported outcome measure to evaluate body image concerns in head and neck cancer (HNC) patients. METHODS: Items were created using a combination of deductive (eg, US Food and Drug Administration Qualification of Clinical Outcome Assessments, literature review) and inductive approaches (eg, subject matter experts, HNC patients). Items were translated for use in both Canadian English and Canadian French using back-translation. A two-step empirical validation process using the Classical Test Theory (CTT) and Rasch Measurement Theory (RMT) was conducted with 224 and 258 HNC patients, respectively, having undergone disfiguring surgery within the past 3 years. RESULTS: Analyses suggest two subscales for MBIS-HNC: social discomfort (10 items) and negative self-image (11 items). The McGill Body Image Concerns Scale-Head and Neck Cancer (MBIS-HNC) is reliable with high internal consistency (0.98), high test-retest reliability over a two-week period (ICC = 0.88), moderate to high convergent validity (range r = 0.43-0.81), and divergent validity (range r = 0.12-0.15). RMT was used in addition to CTT. Disordered thresholds led to the modification of the number of response options, and items were deleted based on differential item functioning and high local dependency. Unidimensionality of both subscales and supporting a total score was confirmed. The measure was however characterized by the presence of an important floor effect, confirmed with poor targeting as demonstrated by the person-item threshold distribution. CONCLUSION: Evidence gathered from our theory-driven validation study using CTT and RMT provides practitioners and researchers with a useful and easy to use self-report measure.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".