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Record W3036882084 · doi:10.12968/ippr.2020.10.2.25

Local socioeconomic status and paramedic students' academic performance

2020· article· en· W3036882084 on OpenAlexaff
Lydia Hamel, Ashley Procum, Justin Hunter, Donna Gridley, Kathleen M. O’Connor, Thomas Fentress, Christopher Goenner, Sahaj Khalsa, Alan M Batt

Bibliographic record

VenueInternational Paramedic Practice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsFanshawe College
Fundersnot available
KeywordsSocioeconomic statusGraduation (instrument)PovertyDisadvantagedPsychologyAcademic achievementMedical educationDemographyMedicineEnvironmental healthSociologyMathematics educationEconomic growthPopulationEconomics

Abstract

fetched live from OpenAlex

Research indicates that students of lower socioeconomic status are educationally disadvantaged. This study sought to examine differences in paramedic students' academic performance from counties with varied socioeconomic status in the United States of America. Student performance data and socioeconomic status data were combined for counties within the states of California, Mississippi, Louisiana, Texas and Virginia. Linear multiple regression modelling was performed to determine the relationship between income, high school graduation rate, poverty and food insecurity, with first-attempt scores on the Fisdap Paramedic Readiness Exam versions 3 and 4. Linear regression models indicated that there was a significant relationship between county-level income, poverty, graduation rate, food insecurity, and paramedic student academic performance. It remains unclear what type of relationship exists between individual socioeconomic status and individual academic performance of paramedic students. These findings support the future collection of individual student socioeconomic data to identify issues and mitigate impact on academic performance.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.134
GPT teacher head0.504
Teacher spread0.370 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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