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Record W4408167685 · doi:10.1016/j.jmir.2025.101864

Tools of the trade: Untangling medical directives, delegations, and the medical radiation technology regulatory landscape in Ontario,✰✰★,

2025· article· en· W4408167685 on OpenAlexaffabout
Darby Erler, Caitlin Gillan

Bibliographic record

VenueJournal of medical imaging and radiation sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of TorontoUniversity Health NetworkSunnybrook Health Science Centre
Fundersnot available
KeywordsMedical radiationBusinessMedicineMedical physics

Abstract

fetched live from OpenAlex

As self-regulated professions, it is incumbent on all disciplines of medical radiation and imaging technologists (MRITs) to maintain an understanding of how their practice is governed, including how its scope of practice and any governing legislation can be leveraged responsibly to optimize practice. With a focus on Ontario, Canada, this paper provides a primer on some of the key regulatory considerations for practice that may be of particular relevance as the profession seeks to optimize its ability to contribute to safe and timely care and to mitigate pressing staffing considerations. In certain Canadian provinces, including Ontario, MRIT disciplines are self-regulated, meaning that practice of the MRIT professions are overseen by a designated self-governing regulatory body in the public interest, pursuant to relevant healthcare legislation in that province. Multiple pieces of legislation govern the practice of MRITs in Ontario; the Regulated Health Professions (RHPA) Act is the legislation that governs all of Ontario's regulated health professions' colleges and identifies controlled acts. The Medical Radiation and Imaging Technology Act (MRIT Act) is the companion act that sets out the scope of practice for the profession and identifies controlled acts that are authorized to MRITs. A medical directive is a document that serves as an order for a procedure, treatment or intervention for a range of patients who meet specific conditions, authorized by a physician, and implemented by another individual. Some practical examples for MRITs include orders for establishing peripheral intravenous access for administration of contrast media (when a medical imaging or radiation therapy simulation scan has been ordered), or administering a pregnancy test (when nuclear medicine exam has been ordered). Delegation is the process by which a regulated health professional authorized to perform a controlled act, gives that authority to someone who is not authorized. Ontario examples include disclosure of results of pregnancy test prior to performing a nuclear medicine exam (RHPA Controlled Act 1) and the prescription of pharmaceuticals from a defined formulary in advanced practice radiation therapy roles (RHPA Controlled Act 8). Medical directives and delegations can be applied responsibly in various MRIT settings, including in standard practice, advanced practice, for MRIT students (and as-yet-uncertified graduates), and for limited practice or assistant MRIT roles.

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)0.019
metaresearch head score (Gemma)0.028
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: none
Teacher disagreement score0.223
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0310.051
Scholarly communication0.0190.007
Open science0.0030.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.008
GPT teacher head0.346
Teacher spread0.338 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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