Putting weight‐related conversations into practice: Lessons learned from implementing a knowledge translation casebook in a disability context
Bibliographic record
Abstract
BACKGROUND: Due to reported challenges experienced by healthcare providers (HCPs) when having weight-related conversations with children with disabilities and their families, a knowledge translation (KT) casebook was developed, providing key communication principles with supportive resources. Our aim was to explore how the KT casebook could be implemented into a disability context. Study objectives were to develop and integrate needs-based implementation supports to help foster the uptake of the KT casebook communication principles. METHODS: A sample of nurses, physicians, occupational therapists and physical therapists were recruited from a Canadian paediatric rehabilitation hospital. Informed by the Theoretical Domains Framework, group interviews were conducted with participants to understand barriers to having weight-related conversations in their context. Implementation strategies were developed to deliver the KT casebook content that addressed these identified barriers, which included an education workshop, simulations, printed materials, and a huddle and email strategy. Participant experiences with the implementation supports were captured through workshop evaluations, pre-post surveys and qualitative interviews. Post-implementation interviews were analysed using descriptive content analysis. RESULTS: Ten HCPs implemented the KT casebook principles over 6 months. Participants reported that the workshop provided a clear understanding of the KT casebook content. While HCPs appreciated the breadth of the KT casebook, they found the abbreviated printed educational materials more convenient. Strategies developed to address participants' need for a sense of community and opportunities to learn from each other did not achieve their aim. Increased confidence in integrating the KT casebook principles into practice was not demonstrated, due, in part, to having few opportunities to practice. This was partly because of the increase in competing clinical demands at the onset of the COVID-19 pandemic. CONCLUSIONS: Despite positive feedback on the product itself, changes in the organisational and environmental context limited the success of the implementation plan. Monitoring and adapting implementation processes in response to unanticipated changes is critical to the success of implementation efforts.
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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 | 0.036 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".