Bibliographic record
Abstract
INTRODUCTION: The dimension of nursing practice in rural areas represents a commitment to solving problems inherent to diverse contexts, and work is influenced by the pleasure-suffering dyad. In this sense, this study aimed to identify and map the factors that cause pleasure and suffering in the work process of rural nurses. This study is important because it sheds light on the unique psychosocial challenges and satisfactions experienced by rural nurses - professionals who are often overlooked in global health discussions. By identifying key factors of pleasure and suffering in the work process or rural nurses, the review supports the development of strategies aimed at promoting mental health and improving professional wellbeing in rural healthcare contexts. METHODS: A scoping review was conducted following the recommendations of the PRISMA Extension for Scoping Reviews, based on the theoretical framework proposed by Arksey and O'Malley. The study protocol was registered via the Open Science Framework. The databases used were PubMed, CINAHL, Scopus, EMBASE, Web of Science, PsycInfo, and LILACS. Also consulted were the following resources: CAPES Thesis and Dissertation Portal, Theses Canada, National EDT, and Google Scholar. There was no temporal delimitation for the study. The results were analyzed under the framework of Christophe Dejours' psychodynamics of work. RESULTS: Approximately 10,689 records were identified in the databases. The final sample consisted of 30 studies, with the majority of studies being scientific articles published between 2007 and 2008. The highest number of publications came from Australia. Regarding the nursing work processes identified in the studies, nursing work processes such as assisting, managing, and teaching were identified. Regarding the factors related to pleasure and suffering in these rural nurse work processes, it was observed that, for example, holistic care, belief in providing quality care and caring for the community were related to pleasure. Factors related to suffering at work included lack of resources, isolation (geographical and professional), and distance from continuing education. CONCLUSION: It is understood that it is not possible to conceive a work organization that, in some way, does not allow the suffering of the professional. Pleasure-suffering results from the interrelation between workers and the work context. Health and pleasure at work are processes that are constantly changing and must be addressed so as not to harm the psychosocial health of workers.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".