Shaping the future rural healthcare landscape: perspectives of young healthcare professionals
Bibliographic record
Abstract
INTRODUCTION: Rural communities continue to struggle to access quality healthcare services. Even in countries where the majority of the population live in rural and remote areas, resources are concentrated in big cities, and this is continuing. As a result, countries with the highest proportion of rural residents correlate with the poorest access, which has negative implications for the health and wellbeing of people. Healthcare professionals (HCPs) have been identified as key informants in the construction and implementation of policies aimed at addressing rural health issues. We sought to understand the perspectives of young HCPs, representing the potential future rural workforce, regarding the future of rural health care. METHODS: An interpretivist paradigm was adopted for the study. Data were collected in two phases over Zoom using semi-structured individual interviews and focus group discussions (FGDs). Participants included selected HCPs who are members of Rural Seeds, which is a global movement for young HCPs. A total of 11 exploratory interviews and six FGDs were conducted. The 11 interviewees consisted of medical doctors and medical students from 10 countries classified at different levels of development by the WHO. The six FGDs ranged from three to nine participants, and they included medical doctors and medical students, nurses and rehabilitation therapists. Participants came from South Asia, Africa, Asia-Pacific, North America and Europe, and South America. Both interviews and FGDs were conducted in English, recorded, and transcribed verbatim. Data were analysed utilising thematic analysis. RESULTS: Similar themes were identified across both individual interviews and FGDs. The state of rural health care was perceived to be problematic by all the participants. Access to care, lack of equity and multiple socioeconomic challenges, particularly in relation to living conditions, human resources and infrastructure, were seen as the most significant issues in rural health care. Several ideas for addressing rural health issues, with examples, were proposed by the young HCPs from their perspectives as frontline healthcare providers. They particularly recognised the importance of addressing the local socioeconomic and developmental needs of rural communities, and the needs of present and future HCPs. CONCLUSION: Young health professionals from across the world interested in a rural career have common concerns about the state of rural health in their countries and constructive insights into how these can be addressed. They suggest effective solutions that must include listening to their voices. This article is a step in that direction.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".