The Feelings About genomiC Testing Results (FACToR) Questionnaire: Development and Preliminary Validation
Bibliographic record
Abstract
The purpose of this study was to develop a brief instrument, the Feelings About genomiC Testing Results (FACToR), to measure the psychosocial impact of returning genomic findings to patients in research and clinical practice. To create the FACToR, we modified and augmented the Multidimensional Impact of Cancer Risk Assessment (MICRA) questionnaire based on findings from a literature review, two focus groups (N = 12), and cognitive interviews (N = 6). We evaluated data from 122 participants referred for evaluation for inherited colorectal cancer or polyposis from the New EXome Technology in (NEXT) Medicine Study, an RCT of exome sequencing versus usual care. We assessed floor and ceiling effects of each item, conducted principal component analysis to identify subscales, and evaluated each subscale's internal consistency, test-retest reliability, and construct validity. After excluding items that were ambiguous or demonstrated floor or ceiling effects, 12 items forming four distinct subscales were retained for further analysis: negative emotions, positive feelings, uncertainty, and privacy concerns. All four showed good internal consistency (0.66-0.78) and test-retest reliability (0.65-0.91). The positive feelings and the uncertainty subscales demonstrated known-group validity. The 12-item FACToR with four subscales shows promising psychometric properties on preliminary evaluation in a limited sample and needs to be evaluated in other populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".