Bibliographic record
Abstract
The research profiled in this issue of CJNL provides important insights into contemporary issues for nursing in Canada.The papers individually and collectively deepen our understanding of why nurses enter nursing, relocate within Canada and remain in or leave their positions and the profession.Informed by the work and experience of the Canadian Nurses Association (CNA), this commentary explores the implications of the work for nursing in Canada. Recruitment into the ProfessionIn their paper on factors that influence career decisions in Canada's nurses, Price and colleagues identify the reasons that nurses enter the profession.The finding that the caring nature of the profession was a key factor in choosing nursing as a career attests to recognition of the association between nursing and caring that has long endured among the public.Indeed, the research found that when nurses felt the time they had to devote to hands-on patient care was inadequate, they became dissatisfied with their jobs and disillusioned with the profession.Further adding to their dissatisfaction with their profession or nursing workplace was the "lack of caring and work ethic" (p. 5) that some respondents perceived among nurses newly entering the profession.Sadly, these respondents stated that their level of dissatisfaction was such that they would not recommend nursing as a career.A profession that fails to attract and retain practitioners cannot be sustained.CoMMeNTARy
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".