36 Quality Improvement Integrated Kangaroo Mother Care (QIiKMC)-Development with Evidence-based Practice for Improving Quality (EPIQ) to Improve Learning and Implementation
Bibliographic record
Abstract
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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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".