6423 Clinical supervision in the basic suturing skill training: the SWOT analysis on trainees’ perception
Bibliographic record
Abstract
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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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".