Satisfaction with civilian family medicine residency training: Perspectives from serving general duty medical officers in the Canadian Armed Forces.
Bibliographic record
Abstract
OBJECTIVE: To evaluate satisfaction with civilian residency training programs among serving general duty medical officers within the Canadian Armed Forces. DESIGN: A 23-item, cross-sectional survey face-validated by the office of the Surgeon General of the Canadian Armed Forces. SETTING: Canada. PARTICIPANTS: General duty medical officers serving in the Canadian Armed Forces as of February 2014 identified through the Directorate of Health Services Personnel of the Canadian Forces Health Services Group Headquarters. MAIN OUTCOME MEASURES: Satisfaction with and time spent in 7 domains of training: trauma, critical care, emergency medicine, psychiatry, occupational health, sports medicine, and base clinic training. Overall preparedness for leading a health care team, caring for a military population, working in isolated and challenging environments, and being deployed were evaluated on a 5-point Likert scale. RESULTS: Among the survey respondents (n = 135, response rate 54%), 77% agreed or strongly agreed that their family medicine residency training was relevant to their role as a general duty medical officer. Most respondents were either satisfied or very satisfied with their emergency medicine training (77%) and psychiatry training (63%), while fewer were satisfied or very satisfied with their sports medicine (47%), base clinic (41%), and critical care (43%) training. Even fewer respondents were satisfied or very satisfied with their trauma (26%) and occupational health (12%) training. Regarding overall preparedness, 57% believed that they were adequately prepared to care for a military patient population, and 52% of respondents believed they were prepared for their first posting. Fewer respondents (38%) believed they were prepared to work in isolated, austere, or challenging environments, and even fewer (32%) believed that residency training prepared them to lead a health care team. CONCLUSION: General duty medical officers were satisfied with many aspects of their family medicine residency training; however, military-specific areas for improvement were identified. Many of these areas might be addressed within the context of a 2-year residency program without risking the generalist nature of family medicine training. These findings provide valuable data for residency programs that accept military trainees across the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".