Setting priorities for rural allied health in Australia: a scoping review
Bibliographic record
Abstract
INTRODUCTION: The allied health workforce is one of the largest workforces in the health industry. It has a critical role in cost-effective, preventative health care, but it is poorly accessible in rural areas worldwide. This review aimed to inform policy and research priorities for increasing access to rural allied health services in Australia by describing the extent, range and nature of evidence about this workforce. METHODS: A scoping review of published, peer-reviewed rural allied health literature from Australia, Canada, the USA, New Zealand and Japan was obtained from six databases (February 1999 - February 2019). RESULTS: Of 7305 no-duplicate articles, 120 published studies were included: 19 literature reviews, and 101 empirical studies from Australia (n=90), Canada (n=8), USA (n=2) and New Zealand (n=1). Main themes were workforce and scope (n=9), rural pathways (n=44), recruitment and retention (n=31), and models of service (n=36). Of the empirical studies, 83% per cent were cross-sectional; 64% involved surveys; only 7% were at a national scale. Rural providers were shown to have a breadth of practice, servicing large catchments with high patient loads, requiring rural-specific skills. Most rural practitioners had rural backgrounds, but rural youth faced barriers to accessing allied health courses. Rural training opportunities have increased in Australia but predominantly as short-term placements. Rural placements were associated with increased likelihood of rural work by graduates compared with discipline averages, and high quality placement experiences were linked with return. Recruitment and retention factors may vary by discipline, sector and life stage but important factors were satisfying jobs, workplace supervision, higher employment grade, sustainable workload, professional development and rural career options. Patient-centred planning and regional coordination of public and private providers with clear eligibility and referral to pathways facilitated patient care. Outreach and telehealth models may improve service distribution although require strong local coordination and training for distal staff. CONCLUSION: Evidence suggests that more accessible rural allied health services in Australia should address three key policy areas. First, improving rural jobs with access to senior workplace supervision and career options will help to improve networks of critical mass. Second, training skilled and qualified workers through more continuous, high quality rural pathways is needed to deliver a complementary workforce for the community. Third, distribution depends on networked service models at the regional level, with viable remuneration, outreach and telehealth for practice in smaller communities. More national-scale, longitudinal, outcomes-focused studies are needed using controlled designs.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".