6423 Clinical supervision in the basic suturing skill training: the SWOT analysis on trainees’ perception
Notice bibliographique
Résumé
Objectives Basic suturing skills are the essential procedural skills in Paediatric emergency medicine.1 The Emergency Department of the KK Women’s and Children’s Hospital, Singapore has adopted the four-component instructional design (4C/ID) for basic suturing training for 3 years. Clinical supervision plays a crucial role in the various aspects of the instructional design such as providing just-in-time information (3rd component of the instructional design) and part-task practice (4th component).2 However, the effectiveness and challenges of clinical supervision have yet to be evaluated. Therefore, this study was conducted with the objectives to explore trainees’ perception of clinical supervision and to identify its strengths, weaknesses, opportunities and threats (SWOT) to clinical supervision in basic suturing skills. Methods We used a survey method to explore the perception of trainees on clinical supervision and conducted focus group discussions (FGD) to explore the trainees’ perception in depth. A descriptive analysis of survey questionnaires and a thematic analysis of FGDs were done for SWOT analysis. Results Forty-five trainees (80.35%) responded to the survey. Thirty-one (55.35%) participated in five FGDs. Sufficient time spent in supervision (82.2%), recognition of mistakes (86.6%) and advice on how to correct them (91.1%), and providing specific (91.1%) and constructive (88.8%) feedback were identified as strengths of clinical supervision. Knowledgeable (93.3%) and approachable (95.5%) supervisors were identified as strengths. Only 46.6% of supervisor presence throughout the procedures was considered by trainees as a weakness in supervision. The parental presence during the procedures, strict supervision and inconsistent instruction among the supervisors were also considered as weaknesses of the instructional design. Most (64.44%) of procedures were supervised by senior residents which was considered as opportunity to incorporate near-peer teaching in the training as trainees felt that the supervision of senior residents was comparable to that of consultants. Trainees valued the supervision by experienced senior nurses and advanced nurse practitioners and it could be an opportunity to modify the training. Supervisors’ availability in only part of the procedures (44.44%) due to busy clinical commitments in emergencies and the limited number of supervisors on shift was considered as a threat to the successful implementation of clinical supervision. Impatient supervisors may also pose as a threat to fruitful clinical supervision. Conclusion Understanding trainees’ perceptions is crucial for good clinical supervision. Modification of instructional design, incorporation of near-peer teaching, a safe learning environment, and trained supervisors are vital for fruitful clinical supervision. References Crickmer M, Lam T, Tavares W, Meshkat N. Do PGY-1 residents in emergency medicine have enough experiences in resuscitations and other clinical procedures to meet the requirements of a Competence by Design curriculum? Canadian Medical Education Journal, 2021;12(3):100–104. doi:doi:10.36834/cmej.70921. van Merriënboer JJG, Clark RE, de Croock MBM. Blueprints for complex learning: the 4C/ID-model. Educational Technology Research and Development, 2022;50(2):39–61. doi:10.1007/BF02504993.
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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 | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».