Concepts Important to Patients With Facial Differences: A Qualitative Study Informing a New Module of the FACE-Q for Children and Young Adults
Bibliographic record
Abstract
OBJECTIVE: The concepts important to children and young adults who undergo treatments for facial differences are not well-defined. Measurement of treatment outcomes from the patient's perspective is necessary to ensure goals of treatment are met. We aimed to identify concepts important to children and young adults with facial differences through a qualitative study. DESIGN: An interpretive description qualitative approach was followed. Semistructured interviews were conducted, transcribed verbatim, and coded using a line-by-line approach. Qualitative analysis led to the development of a conceptual framework of outcomes important to patients. SETTING: Interviews were conducted in Canada and the United Kingdom at home, by telephone, or in the hospital. PARTICIPANTS: Participants (N = 72) were recruited between May and June 2014 from craniofacial clinics at the Hospital for Sick Children (Toronto) and Great Ormond Street Hospital (London). Participants included anyone with a visible and/or functional facial difference aged 8 to 29 years and fluent in English, excluding patients with a cleft. The sample included 38 females and 34 males, with a mean age of 13.9 years, and included 28 facial conditions (11 facial paralysis, 18 ear anomalies, 26 skeletal conditions, and 17 soft tissue conditions). RESULTS: Analysis led to identification of important concepts within 4 overarching domains: facial appearance, facial function, adverse effects of treatment, and health-related quality of life (psychological, social, and school function). CONCLUSIONS: Our study provides an understanding of concepts important to children and young adults with facial differences.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".