Impact of Social Support and Mentoring on Career Advancement of Internationally Educated Nurses
Bibliographic record
Abstract
Background: The upward growth in work level, position, and title, as well as the rise in compensation and income, is known as career advancement (CA). CA is the outcome of career preparation and supportive organizations. Both individual and organizational supports often influence CA among nurses in Canada. Mentorship and social support facilitate CA among nurses, and these supports are available differently to both Canadian Educated Nurses (CENs) and Internationally Educated Nurses (IENs)\n\nPurpose: This study identified the perceived impact of mentorship and social support on CA among CENs and IENs. \n\nMethods: Data were collected utilizing Organizational Career Growth Scale (OCG), Multidimensional Perceived Social Support (MPSS), and Mentoring Functioning Questionnaire (MFQ 9), from 127 nurses across three provinces, namely Ontario, Manitoba, and British Columbia who met the inclusion and exclusion criteria through an online survey. \n\nResults: There were 44 CENs and 83 IENs. The mean score of CENs on OCG was 65.24%, and for IENs, it was 67.68%. The mean score of MPSS for CENs was 76.61%, and for IENs, 73.65%. The mean score on MFQ 9 was 77.84% and 69.11% for CENs and IENs, respectively. There was a positive correlation between MPSS and MFQ 9 with OCG scores. The positive correlation was statistically significant for IENs. With the subscales of OCG, IENs had a statistically significant higher score in remuneration growth (RG) than CENs. In the career growth progress (CGP) subscale, CENs scored higher than IENs. Having a mentor with the title of RN and meeting the mentor regularly positively impacts OCG scores. \n\nConclusion: CENs and IENs have a moderate level of perceived OCG, and IENs have higher scores than CENs. There are differences in the level of mentorship and social support available to CENs and IENs. \n\nRecommendations: Organizations/Employers must establish formal mechanisms to facilitate CA among nurses (both IENs and CENs). Coordinated efforts are necessary to help IENs overcome barriers to accessing support. Establishing formal mentorship programs at the workplace will facilitate better career growth among nurses that will help improve job satisfaction, retention, and, ultimately, quality patient care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".