70 “He's Bugging the Heck out of me…”: A Qualitative Study of the ‘Difficult Patient’ in Pediatric Medical Education
Bibliographic record
Abstract
The ‘difficult patient’ is a well-studied concept in adult medicine that has never been explored in pediatrics. Difficult patient encounters are important learning opportunities. The objectives of this study were to identify ‘difficult’ patients on a pediatric teaching service, and to explore the educational impact of participation in their care. Morning rounds of the pediatric in-patient teaching team at an academic children's hospital were observed and audio-recorded for 4 months (80 hours of observation). Rounds participants included (in rotation) 4 pediatricians, 4 senior residents, 11 junior residents, 11 medical students, 3 pharmacists, and 1 pharmacy resident. Observer effect was minimized by integration of the researcher as a member of the team for the team's entire rotation, and by observation and recording of the entire morning rounds, without apparent focus on ‘difficult’ patient management. During the observed rounds, 128 patients were discussed by team members. Data consisted of observation notes, post-observation reflective notes, and transcripts of relevant rounds discussions. The data were analyzed for emergent themes by three researchers using grounded theory methodology. Analysis identified nine patients (7% of the patients discussed on rounds) who posed sustained and intense difficulty to the team. Markers of difficulty considered in the analysis included verbal labels (“It's just a frustrating kind of case”), non-verbal communication (slumped shoulders, sighs), and length of time spent and emphasis placed on the case during rounds. In the care of the nine identified patients, difficulty arose not only from patient factors, but also from clinical (including diagnostic ambiguity), parent (including challenges of the team's management decisions), professional (including conflict between clinical teams), and systems (including restricted access to investigations) factors. Consistent responses to the difficulty varied from the exclusion of junior trainees from discussions and care, to implicit responses (humor, gestures) that were patient- or parent-related, and explicit discussions that acknowledged multiple dimensions of difficulty and strategized to overcome them (e.g., team discussions about how to proceed in the face of conflicting specialty consultation advice). The ‘difficult patient’ for the pediatric in-patient teaching team is better conceptualized as a case with multiple ‘sources of difficulty’ than as a ‘difficult patient’ per se. Junior trainees risk missing important learning opportunities by exclusion from difficult case management. Attention by clinical teachers to implicit as well as to explicit messages could improve the consistency of the educational impact of the management of these sources of difficulty.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".