Nurses, Soft Skills and Power: Life Stories of Internationally Educated Nurses
Bibliographic record
Abstract
The numbers of internationally educated nurses (IEN) who have joined the Canadian health care workforce have steadily increased since the mid-twentieth century. Much of the literature has framed their nursing knowledge, communication skills, and soft skills from a deficit perspective. Little research has been conducted on IENs and soft skills in the Canadian nursing context and in IENs’ own voices. To address this gap in the literature, this study explored IENs’ interpretations of soft skills and how IENs conform to or resist soft skills in their nursing practice in Canada. The theoretical framework included Foucault’s governmentality, pastoral power, and technologies of the self. It also used transculturation. Data were collected from IENs in Calgary, Alberta, through life story and analyzed through thematic analysis. Findings show that IENs perceive nursing procedures (hard skills) as inseparable from soft skills and soft skills as coming in packages rather than as isolated skills. They view nursing as holistic and use their transcultural knowledge and multilingual abilities to meet the needs of patients from diverse backgrounds. Findings indicate that contrary to the existing soft skills literature, IENs have sophisticated communication and interaction skills, as well as transcultural knowledge. Moreover, findings show that IENs have used their transcultural knowledge and multilingual abilities to challenge the English-only discourse in health care settings. The life stories of the IENs in this study add new perspectives for understanding the relationship between nurses, soft skills, and power. This study suggests that there is a need to find a way to recognize, value, and utilize IENs’ skills and knowledge that does not depend on the biased gatekeeping mechanisms of soft skills for certification and evaluation of nursing 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| 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".