Transitioning to nursing practice in Lebanon: Challenges in professional, occupational and cultural identity formation
Bibliographic record
Abstract
The aim of this study was to identify the challenges graduates from three of Lebanon’s leading universities face as they transition from the role of student to first year registered nurse. Focus group discussions and one joint interview were conducted with 16 first year registered nurses transitioning to practice in university medical centers in Greater Beirut. Thematic analysis was used to summarize the challenges faced by the graduates. Initially, three descriptive themes were used to summarize the data: classroom learning, workplace realities, and “wanting a life”. Together the three themes indicted that classroom instruction of baccalaureate nursing students in Lebanon raises expectations for ideal practice that cannot be realized in clinical units with high workloads and nursing shortages. As a result, first year registered nurses are made to feel unwelcome unless they compromise their values and adapt quickly to the pace of work. The three initial themes were revised deductively from the perspective of ego-identity theory to explain the relationship between transitioning to nursing practice and identity formation in late adolescence and early adulthood. If the pressures of identity formation are not addressed, first year registered nurses in Lebanon will be at risk for acquiescing to task-centered practice, abandoning bedside care for administrative roles, or leaving nursing. The evidence for this conclusion will interest nursing faculty, hospital administrators, nurse leaders, registered nurses, physicians, and nursing students.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".