Continued Influence of an English-as-an-Additional-Language Nursing Student Support Group
Bibliographic record
Abstract
BACKGROUND: English-as-an-additional-language (EAL) nursing students are more likely to experience academic challenges than traditional nursing students whose primary language is English. To support EAL student success, a novel support group was established to address both the academic and nonacademic issues faced by these students. METHOD: A hermeneutic approach was used to explore the perceived influence of a nursing student support group on EAL student success in a Canadian undergraduate nursing setting. Through individual interviews, a rich understanding of the lived experience of EAL nursing students was obtained. RESULTS: The EAL Nursing Student Support Program provided a holistic approach to EAL student success, encompassing both academic and psychosocial support provisions embedded in discipline-specific curricula. Individual interviews regarding support group provisions revealed the perceived importance of balance, resiliency, helping others, culture, a safe place, social aspects, and group environment. CONCLUSION: The continued success of this program necessitates the funding of this support group and other disciplinary support programs that provide comprehensive, discipline-specific approaches to EAL support, arguing against the centralized model of academic aid seen in many postsecondary institutions. [J Nurs Educ. 2019;58(11):647-652.].
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".