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Record W3022380928 · doi:10.7759/cureus.7931

The Impact of a Rotating Elective on Medical Students' Perception of Radiation Oncology

2020· article· en· W3022380928 on OpenAlexaff
Wyatt MacNevin, Todd Dow, Rumana Rafiq, Margaret Man‐Ger Sun, David Bowes

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineRadiation oncologyMedical physicsPerceptionMedical educationOncologyFamily medicineInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

Background As Radiation Oncology (RO) is a field with limited exposure in undergraduate medical education curricula, the information sources used to form students' perception of the field can have a substantial impact on whether students decide to pursue experiences in RO. Furthermore, the effects of a single elective experience in RO can strongly influence career decisions as it may serve as the only experience for students to gain an understanding of RO as a specialty. This study analyzes which information sources students use and most strongly value when forming their perception of RO both before and after participating in the program, while also analyzing changes in the perception of various speciality-related factors associated with RO. Methods To address underrepresented specialties, the Pre-clerkship Residency Exploration Program (PREP) was developed to provide students exposure to RO and 13 other specialties through half-day clinical rotations, simulations, skills sessions, and panel discussions. A total of 37 participants completed both "Pre-program" and "Post-program" surveys to evaluate which information sources they use and value most when forming their perception of RO, and student perception of career factors associated with RO was assessed. Results Students reported that Pre-program information sources of RO were based on Lectures (35 students, 94.6%) and Preceptors (18 students, 48.7%). Post-program responses indicated that the greatest sources of information used were from Preceptors (36 students, 97.3%) and Residents (34 students, 91.9%), with the greatest increase being found in interactions with Residents for gaining specialty information (78% increase). Students most highly valued Preceptors, Residents, and Lectures as information sources when forming their perception of RO. Pre-program, students had the greatest positive perception of RO with respect to Income Potential (mean: 3.76/5.00 ± 0.87), Intellectual Challenge (mean: 3.90/5.00 ± 0.94), and Research Opportunities (mean: 3.86/5.00 ± 0.83) while most negatively assessing the factors of Flexibility (mean: 2.69/5.00 ± 0.93) and Level of Stress (mean: 2.93/5.00 ± 0.94). Conclusions Student perception of a medical specialty is a factor that may influence student elective choice and career decisions. Through participating in PREP, significant positive increases were found in students' perception of RO in the areas of Flexibility, Patient Population, Competitiveness of the Specialty, Quality of the Working Environment, and Levels of Stress. This study highlights which information sources students value the most when forming their perception of RO and the impact a single elective experience has on improving student perception of the field. RO-based programs and lectures can be better designed using this information to introduce students to this specialty.

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)0.003
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.459
Teacher spread0.441 · 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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