'We're it', 'We're a team', 'We're family' means a sense of belonging
Bibliographic record
Abstract
INTRODUCTION: 'Belonging' is a universal characteristic of human beings and is a basic human need. Rural nurses describe the nature of their practice as being embedded in working as a team where belonging is central to the success of the team and the individual nurse. As a result they form close professional and personal ties. The challenge for nursing students is to develop a sense of belonging to the rural hospital team so that preceptorship is successful. OBJECTIVE: To describe the cultural theme of a sense of belonging that nursing students develop during a rural hospital preceptorship. METHODS: Using a focused ethnographic method, a purposive sample of fourth year nursing students and nurse preceptors was drawn from 11 rural communities across central and northern Alberta and Yukon, Canada. Individual interviews and a focus group interview, as well as student journals were analyzed. Ethnographic analysis was used to uncover the system of cultural meaning, 'a sense of belonging' which was the foundation for a successful rural hospital-based preceptorship for the fourth year nursing students. FINDINGS: Nurse preceptors assist students to become members of the team and foster the development of feeling as if they belong by building bridges among the staff and students. For students, the work of being preceptored is developing a sense of belonging. Students feel they belong and are part of the team when they are known personally and professionally. CONCLUSION: Identifying and describing factors that influence students' sense of belonging enhances the effectiveness of the preceptorship model, and increases the potential of recruiting and retaining competent health professionals in the rural hospital setting.
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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 | 0.004 |
| 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 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".