Implementing a comprehensive newborn monitoring chart: Barriers, enablers, and opportunities
Bibliographic record
Abstract
Documenting inpatient care is largely paper-based and it facilitates team communication and future care planning. However, studies show that nursing documentation remains suboptimal especially for newborns, necessitating introduction of standardised paper-based charts. We report on a process of implementing a comprehensive newborn monitoring chart and the perceptions of health workers in a network of hospitals in Kenya. The chart was launched virtually in July 2020 followed by learning meetings with nurses and the research team. This is a qualitative study involving document review, individual in-depth interviews with nurses and paediatricians and a focus group discussion with data clerks. The chart was co-designed by the research team and hospital staff then implemented using a trainer of trainers' model where the nurses-in-charge were trained on how to use the chart and they in turn trained their staff. Training at the hospital was delivered by the nurse-in-charge and/or paediatrician through a combined training with all staff or one-on-one training. The chart was well received with health workers reporting reduced writing, consolidated information, and improved communication as benefits. Implementation was facilitated by individual and team factors, complementary projects, and the removal of old charts. However, challenges arose related to the staff and work environment, inadequate supply of charts, alternative places to document, and inadequate equipment. The participants suggested that future implementation should be accompanied by mentorship or close follow-up, peer experience sharing, training at the hospital and in pre-service institutions and wider stakeholder engagement. Findings show that there are opportunities to improve the implementation process by clarifying roles relating to the filing system, improving the chart supply process, staff induction and specifying a newborn patient file. The chart did not meet the need for supporting documentation of long stay patients presenting an opportunity to explore digital solutions that might provide more flexibility and features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".