Test ordering for preventive health care among family medicine residents.
Bibliographic record
Abstract
OBJECTIVE: To determine which screening tests family medicine residents order as part of preventive health care. DESIGN: A cross-sectional survey. SETTING: Alberta and Ontario. PARTICIPANTS: First- and second-year family medicine residents at the University of Alberta in Edmonton, the University of Calgary in Alberta, and McMaster University in Hamilton, Ont, during the 2011 to 2012 academic year. MAIN OUTCOME MEASURES: Demographic information, Likert scale ratings assessing ordering attitudes, and selections from a list of 38 possible tests that could be ordered for preventive health care for sample 38-year-old and 55-year-old female and male patients. Descriptive and comparative statistics were calculated. RESULTS: A total of 318 of 482 residents (66%) completed the survey. Recommended or appropriate tests were ordered by 82% (for cervical cytology) to 95% (for fasting glucose measurement) of residents. Across the different sample patients, residents ordered an average of 3.3 to 5.7 inappropriate tests per patient, with 58% to 92% ordering at least 1 inappropriate test per patient. The estimated average excess costs varied from $38.39 for the 38-year-old man to $106.46 for the 55-year-old woman. More regular use of a periodic health examination screening template did not improve ordering (P = .88). CONCLUSION: In general, residents ordered appropriate preventive health tests reasonably well but also ordered an average of 3.3 to 5.7 inappropriate tests for each patient. Training programs need to provide better education for trainees around inappropriate screening and work hard to establish good ordering behaviour in preparation for entering practice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".