Putting weight‐related conversations into practice: Lessons learned from implementing a knowledge translation casebook in a disability context
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».