Social Interaction and Collaboration among Oncology Nurses
Bibliographic record
Abstract
Collaboration is a complex process influenced by organizational, professional, interpersonal, and personal factors. Research has demonstrated that collaboration may also be influenced by social factors. Nurses spend much of their time working in collaborative teams, yet little is known about how they socially interact in practice. This qualitative case study explored nurse perceptions of social interaction in relation to collaboration. Data were collected using telephone interviews and documentary reviews from fourteen oncology nurses employed at one cancer center in Canada. Thematic analysis revealed two themes: knowing you is trusting you and formal and informal opportunities. Nurses reported that social interaction meant getting to know someone personally as well as professionally. Social interaction was enacted inside of work during breaks/meals and outside of work at planned events. Social interaction was facilitated by having a long-term current and/or previous professional and personal relationship. The barriers to social interaction included a lack of time to get to know each other, workload issues, and poor interpersonal skills. Findings suggest that social interaction is an important factor in the collaborative relationship among oncology nurses. Nurse leaders need to promote social interaction opportunities and facilitate educational sessions to improve social and interpersonal skills.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".