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Record W3140145145 · doi:10.33151/ajp.18.889

Public Perceptions of the Cost of Paramedic Services in Saskatchewan, Canada

2021· article· en· W3140145145 on OpenAlexaffabout
Adeyemi Ogunade, Florence Luhanga, Jacquie Messer-Lepage, Khan MD Rashed Al-Mamun

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsGovernment (linguistics)BusinessPerceptionFocus groupHealth careEmergency medical servicesService (business)NursingMedical emergencyMedicinePublic relationsMedical educationPsychologyMarketingPolitical science

Abstract

fetched live from OpenAlex

Introduction Despite the increasingly important role of paramedics in Canada's healthcare system, the Canadian Health Act does not cover paramedic services. Anecdotal evidence indicates that the cost of paramedic services prevents many people in need from accessing this care. This article explores public perceptions of the cost of paramedic services in Saskatchewan, Canada. Methods Using a qualitative research design, we collected data from 56 participants in focus group sessions and semi-structured interviews designed to explore perceptions of paramedic services in Saskatchewan. Results The data indicated that participants perceived the cost of paramedic services to be too high, and that this perception may limit the use of paramedic services during medical emergencies. The data also suggested a lack of understanding of how paramedic service costs are calculated. Overall, participants expected the government to do more to subsidise these costs. Conclusion The results revealed a disconnect between public perceptions about the cost of paramedic services and the initiatives designed by the provincial government to alleviate these costs. They also highlight the need for better public education about and access to government programs designed to alleviate the cost of paramedic services.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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