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Record W2916529439 · doi:10.33151/ajp.16.616

Understanding Complaints about Paramedics: A Qualitative Exploration in a Uk Context

2019· article· en· W2916529439 on OpenAlexaff
Grace Lucas, Ann Gallagher, Magdalena Zasada, Zubin Austin, Robert Jago, Sarah Banks, Anna van der Gaag

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Toronto
FundersWelsh Ambulance Services NHS TrustUniversity of Surrey
KeywordsThematic analysisContext (archaeology)Focus groupDelphi methodQualitative researchPublic relationsWork (physics)NursingSet (abstract data type)Medical educationPsychologyMedicinePolitical scienceSociologyEngineering

Abstract

fetched live from OpenAlex

Introduction This research set out to understand the context and explore the reasons for the disproportionate number of complaints raised against paramedics to the United Kingdom professional regulator – the Health and Care Professions Council – relative to other health professions. Methods This paper reports on qualitative findings from one aspect of a mixed-methods study which included a case analysis, Delphi study and literature review. One-to-one semi-structured interviews conducted with 15 stakeholders drawn from practitioners, educators, representatives and regulators, and three focus groups held with 16 practitioners and service users were used to gain an in-depth understanding of the possible reasons for complaints about paramedic practice. Results Five themes were generated from a thematic analysis of the data: the impact of public perceptions and expectations; the challenges of day-to-day practice; the effect of increasingly pressurised services; the organisational and cultural climate which impacts paramedics’ work; and the evolving nature of the profession. Conclusion This study highlights the complex and changing nature of paramedic practice. It provides an insight into the ways in which the character, practice and environment of the profession contribute to a disproportionate number of complaints.

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)0.005
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
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 integrity0.0000.002
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 head0.333
GPT teacher head0.511
Teacher spread0.178 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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