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Record W3176361405 · doi:10.22605/rrh6558

Quality of work life of paramedics practicing community paramedicine in northern Ontario, Canada: a mixed-methods sequential explanatory study

2021· article· en· W3176361405 on OpenAlexaffabout
Behdin Nowrouzi‐Kia, Jordan B Nixon, Stephen D. Ritchie, Elizabeth Wenghofer, David VanderBurgh, Jill E. Sherman

Bibliographic record

VenueRural and Remote Health · 2021
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsNOSM UniversityLakehead UniversityMcMaster UniversityLaurentian University
Fundersnot available
KeywordsNursingPromotion (chess)Medical educationMedicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: This article aimed to evaluate pilot community paramedicine (CP) programs in northern Ontario from the perspectives of paramedics to gain program recommendations related to both rural and urban settings. METHODS: An online questionnaire was created and distributed to 879 paramedics with and without CP experience employed at eight emergency medical services providers in northern Ontario. An explanatory sequential design was used to analyze and synthesize the results from the quantitative survey items and the open-ended responses. RESULTS: Seventy-five (40.5%) respondents participated in a CP program, and the majority of 75 paramedics who indicated they participated in CP (n=41, 54.4%) were from rural areas. CP was generally well received by both paramedics currently practicing CP and those who were not practicing CP. The majority (86.3%) of paramedics stated paramedics should be practicing CP in the future. Paramedics identified developing professional relationships and improving health promotion as positive aspects of CP. Areas for CP program improvement included better organization and scheduling, improved training and a need for better patient tracking software. CONCLUSION: Engaging and consulting paramedics in the ongoing process of CP development and implementation is important to ensure they feel valued and are part of the change process.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.064
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.065
GPT teacher head0.403
Teacher spread0.338 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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