Exploring the Influences of Nurses' Career Decisions to Work in Remote Islands
Bibliographic record
Abstract
Due to global nursing shortages, recruitment and retention of nurses is a major international concern, exacerbated in rural and remote areas. A published paper by Mackay et al. (2021), using systematic review techniques, found that despite the complexity of factors that influence nurses’ decisions to work in rural or remote settings, much of the research to date, mainly conducted in Australia, Canada or USA, has focused on specific factors in isolation. Existing evidence does not fully consider the multiple influences that may impact nurses’ decisions to remain in or leave rural or remote areas. This limits the effectiveness of recruitment and retention strategies. There is a paucity of rural and remote nursing research studies undertaken in the UK. \nThis thesis seeks to address the gap in empirical knowledge by exploring the connections between the person/al, profession/al and place related dimensions of rural and remote nursing and examining the complex interplay between the multiple factors that influence decisions made to work in rural and remote settings over the course of a career. \nUndertaken in the Western Isles of Scotland (WI), twenty-five biographical interviews were conducted with four groups of nurses with different career trajectories: \n-\tGroup 1 (Remainers): Born in WI - trained in WI – remained in WI (n=5) \n-\tGroup 2 (Returners): Born in WI – trained WI or mainland – worked mainland – returned to WI (n=9) \n-\tGroup 3 (Leavers): Born WI or mainland – trained/ worked in WI- left to work on mainland - remained on mainland (n=5) \n-\tGroup 4 (Incomers): Born mainland- trained/ worked on mainland – came to train/work in WI (n=6) \n \nThe use of biographical research methods has underpinned this study as semi-structured interviews captured deep and rich data to understand the career journey of nurses and what influenced their decision-making process to work in the Western Isles. Guided by the development of MacKay’s 3P model (person/al, profession/al, place) and the rich heritage of Rural Nurse Theory, results of Stage One of the analysis identified twenty-eight interrelated themes within the person/al, profession/al and place related dimensions of rural and remote nursing. Results of Stage Two of the analysis highlighted four overarching tensions/paradoxes - Anonymity/Visibility; Longing/Belonging; Ancestry/Acceptance; Specialist/Generalist. \nThis study has added to the body of evidence in Rural Nurse Theory by indicating that a lack of anonymity affected nurses who were born in the WI in a different way from nurses from an urban background and local nurses experienced a stronger sense of belonging than ‘incomers’ who had difficulties being accepted. There is a need to support nurses transitioning to rural practice by strengthening their connections to people and place and adopting initiatives such as mentoring, preceptorship and socialisation through community partners. \nNurses from all the groups acknowledged the need for a broad range of knowledge of different specialities and a strong demonstration of resilience to be able to cope with the scope, variety and unpredictability of rural and remote nursing. This thesis clearly highlighted that all the groups of participants highly commended the training, education and the support from tutors in the Western Isles but early career pathways of nurses were clearly haphazard due to some having difficulties securing permanent employment after completing their training. ‘Incomers’ were influenced to contemplate leaving the WI as they saw little opportunity for career progression or promotion. \nThis thesis proposes that the practice of remote and rural nurses needs to be redefined and considered as a speciality area in its own entity. The term ‘expert generalist’ which has historically provided a foundation theory for rural and remote nurses does not adequately address the deeper, intangible complexities and interplay of practice required in the rural and remote setting, where nurse’s practice is intertwined with and shaped by the community and culture. Giving rural and remote nurses career advancement opportunities through specialist training will result in the recognition of expertise, and a pathway of rural career progression, which may attract nurses to plan a rural career. A whole of community approach to workforce will facilitate opportunities for both family members, and the health professional to be part of the rural and remote community. \n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".