Do doctors know that it takes more than an apple a day? Impact of formal nutrition training on family medicine residents’ nutrition knowledge, confidence, attitudes, and counselling abilities: a single site study
Bibliographic record
Abstract
BACKGROUND: Malnutrition and poor dietary intake are major health challenges today. There are well-established benefits of nutrition interventions, but a lack of formalized nutrition training in medical school and residency. There is also little published information regarding nutrition training impact on residents. Physicians lack knowledge, skills, confidence, and training to effectively counsel in daily practice. Consequently, there is urgent need to improve nutrition training in medicine. METHODS: This pre-post study evaluated the impact of an online nutrition course provided to family medicine residents. Time was provided at Academic Half Day to complete the course as well as pre- and post-course surveys with knowledge tests through SurveyMonkey. Descriptive statistics were used to evaluate responses. The project was approved by the University of Saskatchewan Behavioural Research Board (Beh 4433). RESULTS: Thirteen residents completed the pre-course questionnaire (response rate = 54%). Of these, ten (77%) felt they had received inadequate nutrition training, and all thought patients would benefit from improved nutrition counselling. Six residents completed the post-course questionnaire (response rate = 24%). All post-course respondents thought the course was beneficial and that it should be offered to all Canadian family medicine residents, with majority believing it should be mandatory. Respondents' nutrition knowledge, confidence, beliefs on importance of nutrition counselling, and nutrition counselling in practice appear to increase/improve after training. CONCLUSIONS: Implementation of formal nutrition training during residency is important and has the potential to positively influence family medicine residents' nutrition knowledge, attitudes, and rates of nutrition counselling. RECOMMENDATIONS: Future research with larger sample sizes is needed to support these conclusions and improve nutrition training during residency. Future studies should look at nutrition training in other specialties as well as examine the rate and quality of nutrition counselling after residency completion.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".