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Record W2795654985 · doi:10.22454/fammed.2018.912108

Common Factors Among Family Medicine Residents Who Encounter Difficulty

2018· article· en· W2795654985 on OpenAlex
Natalia M. Binczyk, Оксана Бабенко, Shirley Schipper, Shelley Ross

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFamily Medicine · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFamily medicineMedicineResidency trainingGerontologyDemographyMedical educationContinuing education

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Residents in difficulty are costly to programs in both time and resources, and encountering difficulty can be emotionally harmful to residents. Approximately 10% of residents will encounter difficulty at some point in training. While there have been several studies looking at common factors among residents who encounter difficulty, some of the findings are inconsistent. The objective of this study was to determine whether there are common factors among the residents who encounter difficulty during training in a large Canadian family medicine residency program. METHODS: Secondary data analysis was performed on archived resident files from a Canadian family medicine residency program. Residents who commenced an urban family medicine residency program between the years of 2006 and 2014 were included in the study. RESULTS: Five hundred nine family medicine residents were included in data analysis. Residents older than 30 years were 2.33 times (95% CI: 1.27-4.26) more likely to encounter difficulty than residents aged 30 years or younger. Nontransfer residents were 8.85 times (95% CI: 1.17-66.67) more likely to encounter difficulty than transfer residents. The effects of sex, training site, international medical graduate status, and rotation order on the likelihood of encountering difficulty were nonsignificant. CONCLUSIONS: Older and nontransfer residents may be facing unique circumstances and may benefit from additional support from the program.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.339
Teacher spread0.271 · 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