Understanding nurses’ dual practice: a scoping review of what we know and what we still need to ask on nurses holding multiple jobs
Bibliographic record
Abstract
BACKGROUND: Mounting evidence suggests that holding multiple concurrent jobs in public and private (dual practice) is common among health workers in low- as well as high-income countries. Nurses are world's largest health professional workforce and a critical resource for achieving Universal Health Coverage. Nonetheless, little is known about nurses' engagement with dual practice. METHODS: We conducted a scoping review of the literature on nurses' dual practice with the objective of generating hypotheses on its nature and consequences, and define a research agenda on the phenomenon. The Arksey and O'Malley's methodological steps were followed to develop the research questions, identify relevant studies, include/exclude studies, extract the data, and report the findings. PRISMA guidelines were additionally used to conduct the review and report on results. RESULTS: Of the initial 194 records identified, a total of 35 met the inclusion criteria for nurses' dual practice; the vast majority (65%) were peer-reviewed publications, followed by nursing magazine publications (19%), reports, and doctoral dissertations. Twenty publications focused on high-income countries, 16 on low- or middle-income ones, and two had a multi country perspective. Although holding multiple jobs not always amounted to dual practice, several ways were found for public-sector nurses to engage concomitantly in public and private employments, in regulated as well as in informal, casual fashions. Some of these forms were reported as particularly prevalent, from over 50% in Australia, Canada, and the UK, to 28% in South Africa. The opportunity to increase a meagre salary, but also a dissatisfaction with the main job and the flexibility offered by multiple job-holding arrangements, were among the reported reasons for engaging in these practices. DISCUSSION AND CONCLUSIONS: Limited and mostly circumstantial evidence exists on nurses' dual practice, with the few existing studies suggesting that the phenomenon is likely to be very common and carry implications for health systems and nurses' welfare worldwide. We offer an agenda for future research to consolidate the existing evidence and to further explore nurses' motivation; without a better understanding of nurse dual practice, this will continue to be a largely 'hidden' element in nursing workforce policy and practice, with an unclear impact on the delivery of 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 | 0.043 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".