Technical skills in paediatrics: a qualitative study of acquisition, attitudes and assumptions in the neonatal intensive care unit
Bibliographic record
Abstract
PURPOSE: While the effective acquisition of technical skills is essential for excellent paediatric care, little is known about how technical skills are learned in the paediatric setting. This study sought to describe and theorise the variables influencing technical skills acquisition in a tertiary care neonatal intensive care unit (NICU) inpatient setting. METHODOLOGY: Using non-participant field methodology, paediatric residents and their teachers (nurses, respiratory therapists, neonatal staff and fellows) were observed at various times in the NICU for 8 weeks. Thirteen semistructured interviews with these teachers and learners and 1 focus group of additional learners were conducted and used to triangulate observational findings. Using a constant comparative process, field notes, interview and focus group transcripts were analysed by 2 researchers for emergent themes in the grounded theory tradition. RESULTS: Data sourced from over 90 hours of observation and 21 observed technical procedures, and both individual and group interviews are presented thematically. Dominant themes include: the nature, timing and purpose of feedback about technical procedures; opportunities to learn technical skills; multiple demands that intersect with technical procedure attempts; competing priorities, and teachers' and learners' differing perceptions. These themes interact to affect the learning environment. CONCLUSION: The NICU learning environment represents a complex interplay between competing priorities, learning opportunities and attributions about learners. This interplay must be understood if improvements to technical skills training in this domain are to be developed.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".