70 “He's Bugging the Heck out of me…”: A Qualitative Study of the ‘Difficult Patient’ in Pediatric Medical Education
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,015 | 0,016 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».