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Record W1988659199 · doi:10.1080/00981389.2014.999979

Development of the Emergency Medical Services Role Identity Scale (EMS-RIS)

2015· article· en· W1988659199 on OpenAlexaff
Elizabeth Donnelly, Darcy Clay Siebert, Carl Siebert

Bibliographic record

VenueSocial Work in Health Care · 2015
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsScale (ratio)Identity (music)Emergency medical servicesPsychologyApplied psychologySocial workIntervention (counseling)Exploratory factor analysisPsychometricsClinical psychologyMedicineMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

This article describes the development and validation of the theoretically grounded Emergency Medical Services Role Identity Scale (EMS-RIS), which measures four domains of EMS role identity. The EMS-RIS was developed using a mixed methods approach. Key informants informed item development and the scale was validated using a representative probability sample of EMS personnel. Factor analyses revealed a conceptually consistent, four-factor solution with sound psychometric properties as well as evidence of convergent and discriminant validities. Social workers work with EMS professionals in crisis settings and as their counselors when they are distressed. The EMS-RIS provides useful information for the assessment of and intervention with distressed EMS professionals, as well as how role identity may influence occupational stress.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.358
Teacher spread0.334 · 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 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

Citations29
Published2015
Admission routes1
Has abstractyes

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