Factors influencing rural health care professionals’ access to continuing professional education
Bibliographic record
Abstract
OBJECTIVES: The purposes of this study were to explore the perceived barriers and challenges to continuing professional education (CPE) access for Canadian health care professionals and to identify best practices for improving access to CPE. DESIGN: Key informant interviews and Web-based online surveys were conducted. PARTICIPANTS: Key informant interviews were conducted with national CPE accreditation bodies and health professional associations. An online survey was distributed to health professional education programs, as well as provincial professional associations, licensing and professional regulatory bodies. MAIN OUTCOME MEASURES: The perceived barriers and challenges to CPE access for Canadian health care professionals and best practices for improving access to CPE. RESULTS AND CONCLUSIONS: Geographic isolation and poor technological and telecommunications infrastructure were identified as key barriers to CPE delivery and access. Financial factors, such as funding to support travel or cost of attendance, were also identified as major challenges. Tele-education programming was identified as a best practice approach to improve CPE access, as were regional CPE activities and self-directed learning programs. Employer-sponsored initiatives, including staff coverage or locum support, remuneration for time off and paid travel expenses for CPE participation were also identified as best practice approaches.
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 | 0.001 | 0.017 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".