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Record W4399039439 · doi:10.12968/jpar.2024.16.6.249

Achievement of student paramedic competency in out-of-ambulance settings

2024· article· en· W4399039439 on OpenAlexaffabout
Kurstin Salisbury, Dendra Hillier, Erin Bons, Terri Cadeau, Vanessa Brooker

Bibliographic record

VenueJournal of Paramedic Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsLoyalist College
Fundersnot available
KeywordsEmergency Care PractitionerCombat Medical TechnicianAmbulance serviceMajor traumaContinuing professional developmentMedical emergencyMedicinePsychologyMedical educationProfessional development

Abstract

fetched live from OpenAlex

Competency in key areas is required for job readiness for paramedics, and is monitored by preceptors through in-ambulance placements. Without preceptors, learners are unable to move forward with their training and diploma completion. To address this, an alternative model in community settings to provide students with effective learning opportunities could be explored. This study aimed to evaluate the efficacy of an out-of-ambulance placement model on competency achievement for students in a 2-year primary care paramedic diploma programme at a Canadian college. One cohort of second-year students selfselected a hybrid model of placement, where 6 weeks of training were completed in ambulance and 6 weeks out of ambulance. Competencies were tracked and approved by preceptors, who were certified paramedics and other health professionals. Results were recorded using paramedic competency tracking software and analysed. Compared to traditional placement, the hybrid placement model resulted in a similar average number of competencies being achieved and provided unique opportunities for competency achievement across groups. A hybrid model of placement was shown to have comparable value to a traditional model in competency achievement.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.450
Teacher spread0.405 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes2
Has abstractyes

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