Common Factors Among Family Medicine Residents Who Encounter Difficulty
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".