The Development of an Outreach Educational Workshop for Pediatricians to Manage Newborns With Birth‐Related Neurological Illness
Bibliographic record
Abstract
BACKGROUND: Neonatal encephalopathy (NE), particularly due to hypoxia/ischemia at birth, is a perinatal emergency. Timely diagnosis and initiation of therapeutic hypothermia within a 6-h window from birth are challenging. AIM: We aimed to explore the challenges community physicians face in the management of NE in our region and to test the feasibility of co-designing and implementing an educational program that addresses their needs. METHODOLOGY: This mixed-method study started with a 1 h semi-structured interview with community pediatricians through an audio-recorded virtual platform. Subsequently, an outreach training workshop was designed and pilot-tested at each participating center. DATA ANALYSIS: Qualitative analysis included the following steps: data familiarization, code and theme generation, and report writing. Descriptive statistics were used to summarize the impact of the curriculum on participants' knowledge and experience. RESULTS: Key themes included lack of experience with detailed neurological examination due to infrequent exposure, lack of standardization in management guidelines, and limited access to brain monitoring devices. Diagnosing NE was challenging due to the natural variability in presentation, progression and time-sensitiveness of interventions. Focused skill development workshops were found to be feasible and significantly improved participants' knowledge of NE. Almost 75% of the participants perceived that the workshop enhanced their comfort levels with the neonatal neurological assessment. CONCLUSION: Outreach training education adapted to the local context is feasible and essential for the skill maintenance of community pediatricians who infrequently encounter NE. Future studies will have to study the impact of such workshops on physician behavior and patient outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".