Development of a storytelling communication facilitation tool (SCFT) to facilitate discussion of complex genetic diagnoses between parents and their children: A pilot study using 22q11.2 deletion syndrome as a model condition
Bibliographic record
Abstract
To develop and evaluate a storytelling communication facilitation tool designed to help parents overcome barriers to discussing a complex multisystem genetic diagnosis with their affected children, using 22q11.2 deletion syndrome (22q11DS) as an exemplar condition. A story telling communication facilitation tool (SCFT), entitled 22q and Me, was developed for a target audience of children with 22q11DS aged 9 to 12. The SCFT was evaluated by 14 parents to assess usability and utility by comparing responses to survey questions before and after viewing the SCFT, using a Likert scale. After viewing 22q and Me, parents reported that barriers to discussion were mitigated. Participants indicated they felt more comfortable and better prepared to talk to their children about 22q11DS and worried less that the diagnosis would affect their children’s self-esteem. Parents described 22q and Me as engaging and able to address parental concerns. 22q and Me was found to be an effective tool for increasing parental comfort and ability to talk to their children about their diagnosis of 22q11DS. This novel storytelling communication facilitation tool can serve as a model for the development of other educational tools geared at facilitating disclosure and discussion of other genetic conditions.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".