Set Up to Fail? Barriers Impeding Resident Communication Training in Neonatal Intensive Care Units
Bibliographic record
Abstract
PURPOSE: Learning to navigate difficult clinical conversations is an essential feature of residency training, yet much of this learning occurs "on the job," often without the formative, multisource feedback trainees need. To generate insight into how on-the-job training influences trainee performance, the perspectives of parents and health care providers (HCPs) who engaged in or observed difficult conversations with Neonatal Intensive Care Unit (NICU) trainees were explored. METHOD: The iterative data generation and analysis process was informed by constructivist grounded theory. Parents (n = 14) and HCPs (n = 10) from 2 Canadian NICUs were invited to participate in semistructured interviews informed by rich pictures-a visual elicitation technique useful for exploring complex phenomena like difficult conversations. Themes were identified using the constant comparative approach. The study was conducted between 2018 and 2021. RESULTS: According to participants, misalignment between parents' and trainees' communication styles, HCPs intervening to protect parents when trainee-led communication went awry, the absence of feedback, and a culture of sole physician responsibility for communication all conspired against trainees trying to develop communication competence in the NICU. Given beliefs that trainees' experiential learning should not trump parents' well-being, some physicians perceived the art of communication was best learned by observing experts. Sometimes, already limited opportunities for trainees to lead conversations were further constricted by perceptions that trainees lacked the interest and motivation to focus on so-called "soft" skills like communication during their training. CONCLUSIONS: Parents and NICU staff described that trainees face multiple barriers against learning to navigate difficult conversations that may set them up to fail. A deeper understanding of the layered challenges trainees face, and the hierarchies and sociocultural norms that interfere with teaching, may be the start of breaking down multiple barriers trainees and their clinician supervisors need to overcome to succeed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".