A comparative analysis of the perceived continuing medical education needs of a cohort of rural and urban Canadian family physicians.
Bibliographic record
Abstract
OBJECTIVE: To assess the perceived continuing medical education (CME) needs of a cohort of Canadian family physicians. METHODS: We distributed a questionnaire survey to Canadian family physicians who became Certificant members of the College of Family Physicians in 2001 and practised outside the province of Quebec. Main outcome measures were self-reported CME needs, professional development needs and preferences for CME delivery methods. RESULTS: We distributed 482 surveys and 197 questionnaires were returned for a response rate of 40.9%. Significant differences between rural and urban respondents' self-reported CME needs were found in the clinical areas of dermatology, endocrinology, emergency medicine, musculoskeletal, ophthalmology, otolaryngology, psychiatry and urology. Generally, a greater proportion of rural respondents reported significantly higher CME needs in emergency medicine. Urban respondents reported a significant preference for consulting colleagues as a method of CME, while rural respondents reported a significant preference for videoconferencing. CONCLUSION: Self-reported CME needs and preferences for CME delivery methods differ on the basis of region of practice and size of the community in which family physicians' practise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".