At odds: How intraprofessional conflict and stratification has stalled the Ontario paramedic professionalization project
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".