The role of digital technology in providing education, training, continuing professional development and support to the rural health workforce
Bibliographic record
Abstract
Purpose Education, training and continuing professional development are amongst the evidence-based initiatives for attracting and retaining rural and remote health professionals. With rapidly increasing access to and use of digital technology worldwide, there are new opportunities to leverage training and support for those who are working in rural and remote areas. In this paper we determine the key elements associated with the utility of digital technologies to provide education, training, professional learning and support for rural health workforce outside the University and tertiary sector. Design/methodology/approach A scoping review of peer-reviewed literature from Australia, Canada, US and New Zealand was conducted in four bibliographic databases – Medline complete, CINAHL, Academic Search complete and Education Complete. Relevant studies published between January 2010 and September 2020 were identified. The Levacet al. (2010) enhanced methodology of the Arksey and O'Malley (2005) framework was used to analyse the literature. Findings The literature suggests there is mounting evidence demonstrating the potential for online platforms to address the challenges of rural health professional practice and the tyranny of distance. After analysing 22 publications, seven main themes were found – Knowledge and skills (n = 13), access (n = 10), information technology (n = 7), translation of knowledge into practice (n = 6), empowerment and confidence (n = 5), engagement (n = 5) and the need for support (n = 5). Ongoing evaluation will be critical to explore new opportunities for digital technology to demonstrate enhanced capability and retention of rural health professionals. Originality/value To date there has been limited examination of research that addresses the value of digital platforms on continuing professional development, education and support for rural health professionals outside the university and tertiary training sectors.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".