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Record W1987749209 · doi:10.1186/1472-6920-13-125

Characteristic profiles among students and junior doctors with specific career preferences

2013· article· en· W1987749209 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Medical Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMcGill University
FundersJapan Society for the Promotion of Science
KeywordsSpecialtyPreferenceMedicineWorkforceMedical educationFamily medicinePopularityObstetrics and gynaecologyChartPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Factors influencing specialty choice have been studied in an attempt to find incentives to enhance the workforce in certain specialties. The notion of "controllable lifestyle (CL) specialties," defined by work hours and income, is gaining in popularity. As a result, many reports advocate providing a 'lifestyle-friendly' work environment to attract medical graduates. However, little has been documented about the priority in choosing specialties across the diverse career opportunities.This nationwide study was conducted in Japan with the aim of identifying factors that influence specialty choice. It looked for characteristic profiles among senior students and junior doctors who were choosing between different specialties. METHODS: We conducted a survey of 4th and 6th (final)-year medical students and foundation year doctors, using a questionnaire enquiring about their specialty preference and to what extent their decision was influenced by a set of given criteria. The results were subjected to a factor analysis. After identifying factors, we analysed a subset of responses from 6th year students and junior doctors who identified a single specialty as their future career, to calculate a z-score (standard score) of each factor and then we plotted the scores on a cobweb chart to visualise characteristic profiles. RESULTS: Factor analysis yielded 5 factors that influence career preference. Fifteen specialties were sorted into 4 groups based on the factor with the highest z-score: "fulfilling life with job security" (radiology, ophthalmology, anaesthesiology, dermatology and psychiatry), "bioscientific orientation" (internal medicine subspecialties, surgery, obstetrics and gynaecology, emergency medicine, urology, and neurosurgery), and "personal reasons" (paediatrics and orthopaedics). Two other factors were "advice from others" and "educational experience". General medicine / family medicine and otolaryngology were categorized as "intermediate" group because of similar degree of influence from 5 factors. CONCLUSION: What is valued in deciding a career varies between specialties. Emphasis on lifestyle issues, albeit important, might dissuade students and junior doctors who are more interested in bioscientific aspects of the specialty or have strong personal reasons to pursue the career choice. In order to secure balanced workforce across the specialties, enrolling students with varied background and beliefs should be considered in the student selection process.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.314
Teacher spread0.278 · 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