MO‐D‐301‐01: Joint AAPM/CCPM Sympsium: The Inverse Problem in Medical Physics Training — Defining the Objectives and Finding the Solutions
Notice bibliographique
Résumé
The education and training of medical physicists has lately been a much discussed and somewhat contentious issue. There exists a spectrum of opinions on the optimal definition of the endpoint and on the appropriate pathway to get there. In order to find an optimal solution, a well‐defined objective must be established. What are the salient features of a medical physicist that education and training programs should produce? How do we best balance didactic and clinical training, and what role should research play in training programs? How many medical physicists do we need and will our training programs be able to produce them? What is the status of certification and how does this affect training? This symposium will address the following specific topics, with viewpoints from both American and Canadian speakers. Supply and Demand ‐ Michael D. Mills and Brenda Clark: How many medical physicists do we have? What are the appropriate staffing levels to provide adequate service? How many are we going to need to enter the workforce in the future? The safety of patients treated with radiation oncology associated with personnel credentialing and staffing has become the focus of national and international concern. ASTRO is revisiting the question of personnel staffing levels by organizing and re‐convening the “Blue Book” project; this is the first such effort since 1991. In addition, the International Atomic Energy Agency has convened an effort to establish recommended international staffing recommendations. A previously published supply and demand model for radiation oncology physicists is updated and presented to predict medical physicist employment market parameters through the year 2020. CAMPEP Accredited Programs ‐ Wayne Beckham: What is CAMPEP, what are the requirements for accreditation, and how does the process take place? How many accredited programs are there, and how many applications are pending? What is the future? The Status and Role of Certification ‐ G. Donald Frey and Dave Wilkins What role does certification play in the hiring and career paths of medical physicists? What is the relationship between registration, licensure, and career advancement? How many medical physicists are certified, and is this changing? How many uncertified medical physicists are in clinical practice? What are some of the barriers to certification? Educational Pathways and Training Programs ‐ Michael Herman and Jerry Battista: What should we be striving for in our training programs? What is the correct balance between didactic and clinical training? What role should research play in the training of medical physicists? Panel Discussion: The symposium will close with a panel discussion that will provide an opportunity for audience members to actively participate. Learning Objectives: 1. Understand the need to establish recommended personnel staffing levels for medical physicists 2. Understand a current model that predicts the supply and demand for radiation oncology physicists through 2020. 3. Understand the CAMPEP accreditation process 4. Understand the relevance of certification in the medical physics career path 5. Understand the need to strike a balance between clinical, didactic, and research training in the design of medical physics training programs
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,009 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,028 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».