36 Quality Improvement Integrated Kangaroo Mother Care (QIiKMC)-Development with Evidence-based Practice for Improving Quality (EPIQ) to Improve Learning and Implementation
Notice bibliographique
Résumé
Abstract Introduction/Background Kangaroo Mother Care (KMC) improves outcome for small newborns, but implementation has been slow. This lag may be associated with lack of short, effective learning programs for healthcare workers, and limited ability to overcome barriers to KMC program development. Objectives To develop a practical learning program for KMC with focus on facilitating healthcare workers assisting the learning of mothers and other family members in KMC care of small babies. To integrate KMC with quality improvement to assist healthcare workers overcome barriers to implementation and improve practice. Design/Methods Six neonatologists, with other global experts, developed as simulation-based, interactive, 12-contact-hour learning program with virtual pilot testing in Uganda, Tanzania, and Nepal, prior to implementation in Mbarara Regional Referral Hospital in Uganda. Revisions in QIiKMC course content and integration with quality improvement were accompanied by development of A KMC Readiness, Survey, Knowledge and Confidence Check, Parent Information, and Course Evaluation, with all components available at www.cnf-fnc.ca. Results Thirty-three nurses and physicians increased knowledge scores from 79% to 88% post-learning. 77% indicated the course was useful or very useful, appreciating “The link between EPIQ and KMC in identification and solving problems” and the “Usefulness of family involvement in caring for the newborn in the hospital and home”. Participants indicated preferences for face-to-face learning and more time for hands-on practice. KMC for small babies increased from 0% to 65% (by August-October 2022). Length of hospital stay decreased by 5 days. Government increased KMC beds from 4 to 8. Staff reported increased job satisfaction along with increased quality improvement activities. Family members in addition to mothers were involved (especially with multiple births or if the mother was ill). Families helped other families with learning. One father reported that “When my baby grows up, I will let him know that it was my warmth which kept him alive”. Conclusion Development of a short, practical KMC learning program was feasible. Integration with quality improvement was empowering and impactful. Acknowledgements Funding from the Royal College of Physicians and Surgeons of Canada and a Rotary Global Grant is appreciated. Potential competing interests Funding for learning program development was received from the Royal College of Physicians and Surgeons of Canada. Funding for KMC implementation in Uganda was supported by a Rotary Global Grant.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».