Establishing Content Validity of the CLEFT-Q: A New Patient-reported Outcome Instrument for Cleft Lip/Palate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The CLEFT-Q is a new patient-reported outcome instrument designed to measure outcomes that matter to patients. The aim of this qualitative study was to establish content validity of the CLEFT-Q in patients who differ by age and culture. METHODS: Patients aged between 6 and 29 years were recruited from plastic surgery clinics in Canada, India, Ireland, the Philippines, the Netherlands and the United States. Healthcare providers and other experts participated in a focus group or provided individual feedback. Input was sought on all aspects of the CLEFT-Q (item wording, instructions, and response options), and to identify missing content. Patient interviews and expert feedback took place between September 2013 and September 2014. RESULTS: Sixty-nine patients and 44 experts participated. The first draft of the CLEFT-Q consisted of 163 items measuring 12 constructs. The first round of feedback identified 92 items that required revision. In total, 3 rounds of interviews, and the involvement of an artist to create pictures for 17 items, were needed to establish content validity. At the conclusion of cognitive interviews, the CLEFT-Q consisted of 13 scales (total 171 items) that measure appearance, health-related quality of life, and facial function. The mean Flesch-Kincaid readability statistic for items was 1.4 (0 to 5.2). CONCLUSION: Cognitive interviews and expert review allowed us to identify items that required re-wording, re-conceptualizing, or to be removed, as well as any missing items. This process was useful for refining the CLEFT-Q scales for further testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it