Canadian cancer nurses' views on recruitment and retention
Bibliographic record
Abstract
AIM: The purpose of this study was to explore oncology nurses' perceptions about recruitment and retention. BACKGROUND: Competition among healthcare organizations to recruit and retain qualified nurses is a real-life challenge. Focusing attention on human resource planning in oncology is highlighted by both the worsening nursing shortage and cancer incidence. METHODS: A participatory action research approach was used and 12 focus groups with 91 cancer nurses were conducted across Canada to collect data about strategies that could improve recruitment and retention. RESULTS: Four themes emerged reflecting oncology nurses' beliefs and values about organizational practices that attract and retain nurses and they are as follows: (1) recognizing oncology as a specialty, (2) tacit knowledge no longer enough, (3) gratification as a retaining factor, and (4) relationship dependent on environment. CONCLUSIONS: Participants highlighted leadership, recognition and professional and continuing education opportunities as critical to job satisfaction and organizational commitment. IMPLICATIONS FOR NURSING MANAGEMENT: Recruitment and retention were viewed as a continuum where organizational investment begins with a well-developed orientation and ongoing mentorship to ensure knowledge development. The challenge for nurse leaders is to use the evidence generated from this study and previous studies to develop professional practice environments that facilitate the cultural changes needed to build and sustain a quality nursing workforce.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".