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Record W4399363228 · doi:10.1016/j.tipsro.2024.100256

Empowering radiation therapists: The role of an African Community of Practice in developing radiation Therapist education curriculum

2024· article· en· W4399363228 on OpenAlexaff
Y. Tsang, Kofi Adesi Kyei, Sandra Ndarukwa, Katie Wakeham, Abiola Fatimilehin, Kimyakhanim Bakhinshova, Lisbeth Cordero Mendez

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersInternational Atomic Energy Agency
KeywordsRadiation TherapistCurriculumMedicineMedical educationPsychologyPsychotherapistNursingRadiation therapyPedagogyRadiology

Abstract

fetched live from OpenAlex

Objectives: Supported by the International Atomic Energy Agency (IAEA), the African Regional Cooperative Agreement for Research, Development and Training (AFRA) invited African Member States (MS) with a radiation therapy facility to engage in a 3-day workshop to develop a robust road map for educational standards in radiation therapist (RTT) training. The aim of the paper was to make recommendations of how the African MS could drive forward high educational standards in RTT training and education in Africa. Methods: A pre-workshop survey was developed and sent to the participants to gather background information on each MS's national RTT training standards. An online survey was sent to all African MS with a radiation therapy facility. Two international RTT education-training experts were tasked by the IAEA to support and facilitate the workshop, which consisted of presentations and discussions around the current RTT training schemes in African MS and aspects of modern training methodology. The agenda of the workshop was structured with the aim to simulate discussions on RTT education and training standards among participants from African MS. Results: Sixteen African MS completed the pre-workshop survey. The median number of radiotherapy centres within a MS was 3 (range 1--15). All MS provided two-dimensional radiation therapy services as a minimum while 75 % (12/16) MS could offer three-dimensional conformal radiation therapy service. Thirty-eight percent (6/16) reported that they had no radiation therapy machine service maintenance contracts with vendors and 56 % (9/16) MS had no biomedical engineers on site for unplanned and planned machine maintenance. The median number of RTTs at national level among MS was 23 (range 7-73). Fifty-six percent (9/16) MS had a RTT specific national training programme with 75 % (12/16) MS having clinical attachments for 6 months or more. Representatives from 12 African MS attended the AFRA workshop. An African Community of Practice (CoP) in developing Education Curriculum for RTT was established as an outcome of the workshop with the aim to facilitate knowledge exchange and drive quality initiatives among participating African MS. Four work streams were proposed to form the CoP: RTT academic qualifications, core competencies in RTT education and training, RTT education faculty composition and peer review process in RTT education curricula among African MS. Conclusion: By fostering collaboration, sharing knowledge, and advocating for improved policies, the African COP in developing Education Curriculum for RTT can make significant strides toward developing a RTT education curriculum that not only meets the unique challenges of the African continent but also aligns with global standards.

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)0.038
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.041
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.009
Scholarly communication0.0090.011
Open science0.0030.022
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.421
Teacher spread0.406 · 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 designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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