Factors affecting athletic trainers' and therapists' knowledge and confidence in emergency management
Bibliographic record
Abstract
Introduction: Athletic trainers and therapists (ATTs) are healthcare professionals who specialize in the prevention, assessment, and rehabilitation of injuries. Once certified, they are required to maintain their knowledge via continuing education (CE) to maintain proficiency. However, knowledge has been shown to decay in healthcare professionals, including ATTs, at alarming rates. Confidence in actual knowledge (AK) has been shown to remain even when knowledge decays, indicating an overestimation of perceived knowledge (PK) signifying a knowledge gap. Mandatory CE is limited to Emergency Cardiac Care (ECC) or Basic Life Support (BLS) and does not include the wide scope of emergency management used by ATTs. This study aimed to determine factors affecting ATTs’ knowledge and confidence in three content areas of EM. Methods: A cross-sectional survey was conducted involving 315 ATTs in the United States and Canada (age=36.87±9.96, males=135, females=171, other gender=9, ATC=137, CATC=87, dual certified=89). Participants completed demographic information, pre- and post-test confidence measures, and a knowledge assessment surrounding ECC/BLS, patient assessment (Assessment), and airway management with supplemental oxygen (Airway&O2). The survey was validated by a Delphi method and obtained a CVI of 0.99 and a Cronbach’s alpha of 0.974. Various non-parametric statistical methods were employed to analyze the data, with an alpha of p=.05. Results: Results indicate mandatory CE is effective in limiting knowledge decay and AK increases with years certified, with varying significance between content areas. Airway&O2 demonstrated the lowest confidence and competence scores. Higher AK scores were better correlated with more accurate self-assessment. Confidence was not heavily influenced by completing a knowledge assessment; thus, most ATTs were found to be unconsciously incompetent in emergency management. Additionally, a knowledge gap was determined based on the poor correlation between confidence and competence scores throughout. Conclusions: Mandatory CE works to slow knowledge decay in ATTs and those with more knowledge are better able to self-assess their perceived knowledge and rate their confidence. As such, ATTs should aim to regularly practice self-evaluation to identify areas of weakness, and to seek frequent CE that enhances their knowledge within a variety of content areas to ensure clinical proficiency.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".