Learning to Transition: Nurses' Entry into Cancer Nursing Practice
Bibliographic record
Abstract
In the 21st century, the delivery of cancer care is facing unprecedented challenges, including an increasing number of cancer patients, a shortage of nursing personnel, a shift in care from in-patient to outpatient facilities, and new technologies requiring additional resources and education. The purpose of this critical qualitative study was to explore how nurses learn to transition into cancer nursing practice (CNP) in the workplace. The inquiry examined the contextual and learning factors that enhanced or impeded the nurses’ transition into diverse cancer settings. A comprehensive literature review was conducted in three areas: workplace identity and transitions; social learning theories and informal learning in nursing practice; and the context of cancerland, namely, cancer system, cancer patients’ experience, and cancer nursing as a specialty. Participants completed a preinterview questionnaire that determined whether they met the criteria and were representative of the phenomenon being studied. Telephone interviews were conducted with 15 nurses with more than 3 months and less than 2 years working in 1 of 4 cancer facilities in Ontario. An interpretive, phenomenological approach was used to formulate a description of the newly hired nurses’ lived experience. Three overarching themes emerged unique to CNP: (a) Getting In - nurses perceptions of their recruitment and selection into CNP; (b) Surviving In - nurses’ struggles learning CNP and the emotional strain of “being with” critically ill and dying patients; and (c) Staying In - factors that impacted the nurses’ decision to stay or leave, such as effective nursing leadership, quality of work life, and accessibility of supports (preceptors and mentors) and professional education. The findings will assist nursing leaders, educators, and preceptors when developing strategies to enhance the recruitment, orientation, and education of nurses into CNP. The review included a description of the ways in which the nurses perceived their new role, as well as the rewards and difficulties they encountered as they coped during their first few months of practice. Also included were descriptions of the ways in which the nurses learned to transition into the different cancer nursing subspecialties of in-patient; outpatient; chemotherapy; radiation therapy; and urban, rural, and remote settings.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".