Bibliographic record
Abstract
From November 3–5, 2005, the Academy of Canadian Executive Nurses was involved in three meetings in Ottawa: ACEN’s Education Conference on November 3 and its Annual General Meeting on November 5. On November 4, ACEN members were invited to join the Association of Canadian Healthcare Organizations (ACAHO) at its invitational conference. The focus of the ACEN Education Conference was on nursing human resources planning within the context of the recommendations made in the report of the first phase of the Nursing Sector Study. Fourteen presentations were given by ACEN members, describing local and provincial/territorial initiatives to retain current nursing staff and to recruit new staff. All presentations were based on an analysis of projected nursing human resources requirements in large healthcare organizations and regional authorities from all parts of the country. The stark reality was that each and every healthcare organization will fall short of meeting its nursing resources needs unless something significant is done to retain current nurses and to recruit new graduates. Although we have been aware of the forecasted shortage of nurses from a national perspective, it was a harsh reminder to hear the projected numbers required to meet needs at the local, institutional level. Participants agreed that meeting the nursing needs of Canadians over the next several years may be one of the greatest challenges facing the profession. Given the number of reports and recommendations concerning health human resources that have been published over the past few years, it was reassuring to hear about the many initiatives being implemented by governments and, more importantly, by healthcare employers aimed at addressing these problems. What follows are highlights from the Education ACEN UPDATE
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".