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Record W4294816830 · doi:10.1080/10903127.2022.2120934

Deriving National Continued Competency Priorities for Emergency Medical Services Clinicians

2022· article· en· W4294816830 on OpenAlexaff
Mark A. Terry, Jonathan R. Powell, W. Scott Gilmore, David P. Way, Andrew C. Dwyer, Farhan Bhanji, Ashish R. Panchal

Bibliographic record

VenuePrehospital Emergency Care · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsThematic analysisDelphi methodMedicineMedical educationCertificationContent analysisLikert scaleDescriptive statisticsChecklistDelphiEmergency medical servicesFamily medicineNursingMedical emergencyPsychologyQualitative researchManagement

Abstract

fetched live from OpenAlex

OBJECTIVE: Continued competency is poorly defined in emergency medical services (EMS), with no established method for verifying continued competency at a national level. The objective of this project was to refine understanding of continued competency for EMS clinicians in the U.S. and establish priorities for developing competency assessments. METHODS: A panel of EMS managers, educators, medical directors, and experts in competency assessment, simulation, and certification used a modified Delphi technique to address two questions: "What is the content for continued competency in EMS that should be assessed or verified?" (content) and "How should continued competency of EMS clinicians be demonstrated?" (process). The Delphi process was conducted through electronic conferencing and survey software over a 6-month period. In round one, panelists responded to open-ended prompts and their contributions were analyzed and categorized into themes by independent reviewers. In round two, the panel rated theme importance using five-point Likert-type scales. In round three, the panel ranked their top 10 themes, and in round four, the panel selected the most important themes for each of the two questions through consensus-building discussions. Descriptive statistics and thematic analyses were performed with Excel and STATA 16. RESULTS: Fourteen invited experts participated in all Delphi activities. The panel contributed 70 content and 35 process items from the original prompts. Following thematic analysis, these contributions were reduced to 21 and 14 unique themes, respectively. The final top five prioritized themes for content important for continued competency included (1) airway, respiration, and ventilation, (2) patient assessment, (3) pharmacology, (4) pediatrics, and (5) management of time critical disease progressions. The final top five prioritized themes for the processes for continued competency assessment included (1) assessments of evidence-based practice, (2) performance-based assessments, (3) combined knowledge and skill assessments, (4) performance improvement over time, and (5) frequent, short knowledge assessments. CONCLUSION: This modified Delphi process identified priorities for content and assessment, laying the groundwork for EMS continued competency at a national level. These findings can be leveraged by national task forces to develop transparent and consistent guidelines for systems that verify continued competency related to certification, licensure, and local credentialing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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.

Opus teacher head0.025
GPT teacher head0.376
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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