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Record W2940288537 · doi:10.12968/jpar.2019.11.4.cpd1

Empathy in paramedic practice: an overview

2019· article· en· W2940288537 on OpenAlexaff
Lucy Kus, Lisa Henderson, Alan M Batt

Bibliographic record

VenueJournal of Paramedic Practice · 2019
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsFanshawe College
Fundersnot available
KeywordsEmpathyBurnoutEmergency Care PractitionerFeelingCombat Medical TechnicianPsychologyInterpersonal communicationCoping (psychology)Clinical psychologyMedicineContinuing professional developmentSocial psychologyProfessional development

Abstract

fetched live from OpenAlex

Empathy is generally considered to be the understanding of another person's reactions, thoughts, feelings and problems, and being able to relay this sense of understanding back to the individual. Empathy in healthcare is associated with improved communication, reduced stress, lower complication rates and better clinical outcomes. Low empathy is associated with decreased patient satisfaction and provider burnout. The burden of emotional work in paramedic practice and coping strategies may be contributory factors to lower empathy. Some evidence suggests that the empathy of paramedic students varies between patient groups and can decline over time. Empathy is an interpersonal skill that can be learned and improved upon. In paramedic practice, it is complex and inadequately studied. Its relationship to patient care, paramedic burnout and wellbeing require investigation. Several strategies to teach empathy should be considered by educators.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.049
GPT teacher head0.432
Teacher spread0.383 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2019
Admission routes1
Has abstractyes

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