Canadian Oncology Nurse Work Environments: Part II
Bibliographic record
Abstract
In the aftermath of healthcare restructuring, it is important to pay attention to nurses' perceptions of workplace and professional practice factors that attract nurses and influence their retention. Continuing constraints on cancer care systems make the issue of health human resources an ongoing priority. This paper presents the findings of a follow-up study of a cohort of Canadian oncology nurses that aimed to compare nurses' perceptions of their work environment, job satisfaction and retention over a two-year period. Participants of the follow-up survey represented 65% (397/615) of the initial cohort. Many similar perceptions about the work environment were found over two years; however, at follow-up a larger proportion of nurses reported an absence of enough RNs to provide quality care and a lack of support for innovative ideas. With respect to career status, only 6% (25/397) of the follow-up sample had left oncology nursing. However, the proportion of nurses declaring an intention to leave their current job increased from 6.4% (39/615) on the initial survey to 26% (102/397) on the follow-up survey. Findings suggest that decision-makers need to use both the growing body of workplace knowledge and the input from staff nurses to implement changes that positively influence nurse recruitment and retention. Future research should focus on the implementation and evaluation of strategies that address workplace issues such as nurse staffing adequacy, leadership and organizational commitment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".