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Record W4298148696 · doi:10.12968/ippr.2022.12.3.45

Challenges and changes with COVID-19: Canadian paramedics' experiences

2022· article· en· W4298148696 on OpenAlexaffabout
Lindsey Boechler, Polly Ford-Jones, Jana Smith, Patrick Suthers, Cheryl Cameron

Bibliographic record

VenueInternational Paramedic Practice · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsHumber PolytechnicSaskatchewan Polytechnic
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Mental health2019-20 coronavirus outbreakPublic healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineNursingOutbreakPsychiatry

Abstract

fetched live from OpenAlex

Background: Healthcare systems, practitioners and communities have experienced momentous change and strain because of the COVID-19 pandemic. Although paramedics are an essential component of the public health emergency response, the literature has focused primarily on the views of physicians, nurses and hospital administrators. Aims: This research sought to improve the understanding of the lived experiences of paramedics throughout the initial stages of the COVID-19 outbreak. Methods: The perspectives of paramedics were captured through an online survey consisting predominantly of open-ended questions. Findings: Three main themes describing the experiences of paramedics arose: challenges with change management; changes in day-to-day operations; and implications for mental health. Conclusion: This study has offered insights for future pandemic response in terms of information dissemination, practitioner involvement in policy and operational changes, and mental health and wellbeing support needs during and beyond a pandemic.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.134
GPT teacher head0.465
Teacher spread0.331 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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