Exploring occupational therapy practice with children who are picky eaters and their families
Bibliographic record
Abstract
Introduction: Picky eating is a complex phenomenon, impacting family routines and relationships. Occupational therapists often work with picky eaters and their families, yet little is understood about the occupational therapy process and reasoning in this context. This study was guided by the following research question: How do Australian occupational therapists choose and deliver interventions for children with picky eating and their families? Method: This qualitative interpretive descriptive study used in-depth semi-structured online interviews with 10 Australian-based occupational therapists working with children who are picky eaters. Data was analysed inductively following a thematic analysis process, and emergent themes were identified. Findings: Participants indicated that they used a complex reasoning process, with 'Tailoring Occupational Therapy for Picky Eating' emerging as the central finding. Key factors underpinning these tailored interventions were finding the why; addressing the why; and practising within context. Conclusion: To our knowledge, this is the first qualitative study to investigate occupational therapists' reasoning processes when working with families impacted by picky eating. Occupational therapists described the complexity of picky eating, and the subsequent reasoning to find suitable interventions. Findings may guide occupational therapists' clinical practice when working with children with picky eating and their families.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".