The Development of a Critical Care Resident Research Curriculum: A Needs Assessment
Bibliographic record
Abstract
Background. Conducting research is expected from many clinicians' professional profile, yet many do not have advanced research degrees. Research training during residency is variable amongst institutions and research education needs of trainees are not well understood. Objective. To understand needs of critical care trainees regarding research education. Methods. Canadian critical care trainees, new critical care faculty, program directors, and research coordinators were surveyed regarding research training, research expectations, and support within their programs. Results. Critical care trainees and junior faculty members highlighted many gaps in research knowledge and skills. In contrast, critical care program directors felt that trainees were prepared to undertake research careers. Major differences in opinion amongst program directors and other respondent groups exist regarding preparation for designing a study, navigating research ethics board applications, and managing a research budget. Conclusion. We demonstrated that Canadian critical care trainees and junior faculty reported gaps in knowledge in all areas of research. There was disagreement amongst trainees, junior faculty, research coordinators, and program directors regarding learning needs. Results from this needs assessment will be used to help redesign the education program of the Canadian Critical Care Trials Group to complement local research training offered for critical care trainees.
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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 | 0.080 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".