Faculty Development Approaches for Life Support Courses: A Scoping Review
Bibliographic record
Abstract
The aim of this scoping review initiated by the Education, Implementation and Teams Task Force of the International Liaison Committee on Resuscitation was to identify faculty development approaches to improve instructional competence in accredited life support courses. We searched PubMed, Ovid Embase, Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Central Register of Controlled Trials to identify studies published from January 1, 1966 to December 31, 2021 on approaches to improve faculty development for life support courses. Data on participant characteristics, interventions, design, and outcomes of included studies were extracted. Of the initially identified 10 310 studies, we included 20 studies (5 conference abstracts, 1 short communication, 14 full-length articles). Among them, 12 studies aimed to improve instructors/candidates' teaching ability in basic life support courses. A wide variety of interventions were identified. The interventions were categorized into 4 themes: instructor qualification/training (n=9), assessment tools (n=3), teaching skills enhancement (n=3), and additional courses for instructors (n=5). Most studies showed that these interventions improved specific teaching ability or confidence of the instructors and learning outcomes in different kinds of life support courses. However, no studies addressed clinical outcomes of patients. In conclusion, the faculty development approaches for instructors are generally associated with improved learning outcomes for participants, and also improved teaching ability and self-confidence of the instructors. It is encouraged that local organizations implement faculty development programs for their teaching staff of their accredited resuscitation courses. Further studies should explore the best ways to strengthen and maintain instructor competency, and define the cost-effectiveness of various different faculty development strategies.
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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 | 0.042 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".