Exploration of Learning Preferences for Parents of Children with Sensory Over-Responsivity: A Mixed-Methods Study
Bibliographic record
Abstract
Importance: Parent education is essential to pediatric occupational therapy (OT), yet there is a paucity of research regarding how to best support parents as adult learners. Objective: To explore the learning experiences of parents of children with sensory over-responsivity (SOR). Methods: An embedded mixed-methods design was used. Sixteen parents of children with SOR, aged 3–8 years, were recruited using purposive sampling. All parents participated in group trainings; of these, nine participated in one-on-one interviews. Quantitative data included pre-intervention parenting self-efficacy (PSE) scores and post-intervention learner satisfaction measures. Semi-structured interviews were used to explore parents’ learning experiences during group trainings. Intervention: Group trainings included varied learning opportunities focused on a problem-solving tool, A SECRET, to help parents support their children in varied contexts. Results: Five themes were extracted from interview data: 1) Learning process, 2) Learning challenges, 3) Fluctuating PSE, 4) Peer support, and 5) Future training suggestions. Quantitative analysis indicated no correlation between PSE and learning satisfaction. Mixing of methods provided support for understanding fluctuating PSE. Conclusions and Relevance: This study provides insight into parents' learning preferences and suggests A SECRET may enhance parent training. Findings indicate PSE may fluctuate for parents of children with SOR, warranting further investigation into both A SECRET's effectiveness and PSE's potential impact on parent learning.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".