Decision coaching using a patient decision aid for youth and parents considering insulin delivery methods for type 1 diabetes: a pre/post study
Bibliographic record
Abstract
BACKGROUND: Choice of insulin delivery for type 1 diabetes can be difficult for many parents and children. We evaluated decision coaching using a patient decision aid for helping youth with type 1 diabetes and parents decide about insulin delivery method. METHODS: A pre/post design. Youth and parent(s) attending a pediatric diabetes clinic in a tertiary care centre were referred to the intervention by their pediatric endocrinologist or diabetes physician between September 2013 and May 2015. A decision coach guided youth and their parents in completing a patient decision aid that was pre-populated with evidence on insulin delivery options. Primary outcomes were youth and parent scores on the low literary version of the validated Decisional Conflict Scale (DCS). RESULTS: Forty-five youth (mean age = 12.5 ± 2.9 years) and 66 parents (45.8 ± 5.6 years) participated. From pre- to post-intervention, youth and parent decisional conflict decreased significantly (youth mean DCS score was 32.0 vs 6.6, p < 0.0001; parent 37.6 vs 3.5, p < 0.0001). Youth's and parents' mean decisional conflict scores were also significantly improved for DCS subscales (informed, values clarity, support, and certainty). 92% of youth and 94% of parents were satisfied with the decision coaching and patient decision aid. Coaching sessions averaged 55 min. Parents (90%) reported that the session was the right length of time; some youth (16%) reported that it was too long. CONCLUSION: Decision coaching with a patient decision aid reduced decisional conflict for youth and parents facing a decision about insulin delivery method.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".