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Record W4309671418 · doi:10.1093/jpo/joac016

At odds: How intraprofessional conflict and stratification has stalled the Ontario paramedic professionalization project

2022· article· en· W4309671418 on OpenAlexafffundabout
Madison Brydges, James R. Dunn, Gina Agarwal, Walter Tavares

Bibliographic record

VenueJournal of Professions and Organization · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsThe Wilson CentreRegional Municipality of NiagaraHamilton Health SciencesUniversity of TorontoUniversity Health NetworkSt. Michael's HospitalMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProfessionalizationGovernment (linguistics)Political scienceOddsPublic relationsPower (physics)Public administrationMedicineLaw

Abstract

fetched live from OpenAlex

Abstract Historically, self-regulation has provided some professions with power and market control. Currently, however, governments have scrutinized this approach, and priorities have shifted toward other mandates. This study examines the case of paramedics in Ontario, Canada, where self-regulation is still the dominant regulatory model for the healthcare professions but not for paramedics. Instead, paramedics in Ontario are co-regulated by government and physician-directed groups, with paramedics subordinate to both. This paper, which draws on interviews with paramedic industry leaders analyzed through the lens of institutional work, examines perspectives on the relevance of self-regulation to the paramedic professionalization project. Participants had varying views on the importance of self-regulation in obtaining professional status, with some rejecting its role in professionalization and others embracing regulatory reform. Because paramedics disagree on what being a profession means, the collective professionalization project has stalled. This research has implications for understanding the impact of intraprofessional relationships and conflict on professionalization projects.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.307
Teacher spread0.264 · 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 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

Citations16
Published2022
Admission routes3
Has abstractyes

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