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Emerging Concepts in the Paramedicine Literature to Inform the Revision of a pan-Canadian Competency Framework for Paramedics: A Restricted Review

2022· review· en· W4310911010 on OpenAlexaffabout
Jennifer Bolster, Priya Pithia, Alan M Batt

Bibliographic record

VenuePreprints.org · 2022
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsFanshawe College
Fundersnot available
KeywordsEquity (law)Inclusion (mineral)PsychologyHealth careIdentification (biology)Grey literatureMedical educationMEDLINEMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The National Occupational Competency Profile (NOCP) – the competency framework for paramedics in Canada – is presently undergoing revision. Since the NOCP was published in 2011, paramedic practice, healthcare and society have changed dramatically. To inform the revision, we sought to identify emerging concepts in the literature that would inform the development of competencies for paramedics. We conducted a restricted literature review and content analysis of all published and grey literature pertaining to or informing Canadian paramedicine from 2011 to 2022. Three authors performed a title and abstract, and full-text review to identify and label concepts informed by existing findings. A total of 302 articles were categorized into eleven emerging concepts related to competencies: Inclusion, Diversity, Equity, and Accessibility (IDEA) in paramedicine; Social responsiveness, justice, equity and access; Anti-racism; Healthy Professionals; Evidence Informed Practice and Systems; Complex Adaptive Systems; Learning Environment; Virtual Care; Clinical Reasoning; Adaptive Expertise; and Planetary Health. This review identified emerging concepts to inform the development of the 2023 National Occupational Standard for Paramedics (NOSP). These concepts will inform data analysis, development group discussions, and competency identification.

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)0.007
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.243
GPT teacher head0.552
Teacher spread0.309 · 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 designOther design
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes2
Has abstractyes

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