Bibliographic record
Abstract
The development of leadership positions in nursing is paramount to strengthening the Canadian healthcare system and is one of ACEN’s priorities. National reports, such as the Romanow Report (2002) from the Commission on the Future of Health Care and the 2003 First Ministers Health Accord (Health Canada 2006), have highlighted the importance of building leadership capacities among healthcare professionals. In response to this need, the Canadian Health Services Research Foundation (CHSRF) launched the program Executive Training for Research Application (EXTRA) in 2004. EXTRA is an interprofessional two-year competitive fellowship program. Its twofold goal is to enhance the skills of Canadian healthcare executives (nurses, physicians and health administrators) to use existing research in their dayto-day decision-making and thereby increase the use of evidence-based decision-making overall. EXTRA’s accomplishments have led organizations to encourage those among their leadership ranks to take advantage of the program as one means to attain their own succession planning goals, and also to build capacity in their organizations for research literacy and use. The Halifax-based Capital District Health Authority (CDHA) has supported five EXTRA fellows and has made a commitment to continue to encourage its leadership team to participate in EXTRA. Dr. Judith Kazimirski, Vice President of Medicine for CDHA, said, “There is absolutely no question that this health authority has benefited as much from this program as have the participants themselves.” Now in its third year, EXTRA has accepted 76 fellows, one-third of whom were from nursing. The success of the fellowship is a direct result of the advantages that fellows have derived from their participation and work in EXTRA. Some benefits of EXTRA, in the participants’ own words: ACEN UPDATE
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".