Developing clinician expertise in paediatric dysphagia: what is an effective learning model?
Bibliographic record
Abstract
Background: Developing and maintaining clinician expertise in specialized rehabilitation practice areas can be challenging, and requires effective education and learning strategies. To widen the scope of this series, this article looks at education and learning models, how to develop and assess them, and their value in practice. This article provides an example relevant to any specialized learning. Content: The authors use the example of paediatric feeding and swallowing difficulties (dysphagia) to describe the evaluation and exploration of the education and learning models at two paediatric agencies in British Columbia, Canada. These models are designed to support occupational therapists and speech language pathologists in developing expertise for performing videofuoroscopic swallow studies (VFSS), a component of a comprehensive paediatric dysphagia evaluation. Clinicians involved in providing specialized paediatric dysphagia services, including VFSS, were engaged in this study through a participatory action research approach. Data on current education and learning models and clinicians’ experiences were collected through questionnaires and a focus group. Results identified three interconnected themes: ‘dynamic and interactive learning is necessary to refect the complex and changing practice environment’; ‘supports and resources provide infrastructure to enhance learning’; and ‘personal characteristics influence learning.’ Conclusions: Findings from this preliminary participatory action research study suggest that education and learning models to facilitate the development of clinician expertise in paediatric VFSS should consider employing a multi-faceted approach in which the necessary supports, resources and interactive learning environments are available for both new and experienced clinicians.
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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 | 0.039 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".