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Record W7624827

Medical students, money, and career selection: students' perception of financial factors and remuneration in family medicine.

2009· article· en· W7624827 on OpenAlexaff
Dante Morra, Glenn Regehr, Shiphra Ginsburg

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsThe Wilson CentreToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsRemunerationSpecialtyPerceptionDebtPaymentAffect (linguistics)Family medicineFamily incomePsychologyStudent debtSelection (genetic algorithm)Medical educationMedicineFinanceBusinessPolitical science
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Medical students have had a declining interest in family medicine as a career. Some studies have shown a small inverse relationship between debt levels and primary care, but it is unclear how students perceive remuneration in different specialties and how these perceptions might influence career choice. METHODS: Medical students at one school were surveyed to understand their perceptions of physician remuneration and to gain insight into how these perceptions might affect career selection. RESULTS: Response rate was 72% (560/781 students). Students' estimates of physician income were accurate throughout training, with the overall estimate for family medicine being lower than the actual income by only $10,656. The vast majority of students agreed with the statement that family physicians get paid too little (85%-89% of each class). The importance of payment as a factor in career decision making increased with higher debt and with advancing training. CONCLUSIONS: Students are able to accurately predict income by specialty from an early stage of training and have a negative perception of income in family medicine. The perception that family physicians make too little money could be an important driver--or at least a modifier--in the lack of interest in family medicine.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.071
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.297
Teacher spread0.264 · 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 teacher head, 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

Citations81
Published2009
Admission routes1
Has abstractyes

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