Continuing education for primary health care nurse practitioners in Ontario, Canada
Bibliographic record
Abstract
The Council of Ontario University Programs in Nursing offers a nine-university, consortium-based primary health care nurse practitioner education program and on-line continuing education courses for primary health care nurse practitioners. Our study sought to determine the continuing education needs of primary health care nurse practitioners across Ontario, how best to meet these needs, and the barriers they face in completing continuing education. Surveys were completed by 83 (40%) of 209 learners who had participated in continuing education offered by the Council of Ontario University Programs in Nursing between 2004 and 2007. While 83% (n = 50) of nurse practitioners surveyed indicated that continuing education was extremely important to them, they also identified barriers to engaging in continuing education offerings including; time intensity of the courses, difficulty taking time off work, family obligations, finances and fatigue. The most common reason for withdrawal from a continuing education offering was the difficulty of balancing work and study demands. Continuing education opportunities are important to Ontario primary health care nurse practitioners, and on-line continuing education offerings have been well received, but in order to be taken up by their target audience they must be relevant, readily accessible, flexible, affordable and offered over brief, intense periods of time using technology that is easy to use and Internet sites that are easily navigated.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".