Retrospective bibliometric review of rural health research: Australia's contribution and other trends
Bibliographic record
Abstract
INTRODUCTION: The health of half of the world's 6 billion people and of the 6 million Australians living in rural and remote communities is demonstrably poorer than that of their metropolitan counterparts. As the existence of the discrete specialty of rural health (RH) is gaining acceptability worldwide, publications about RH issues are increasing in prevalence. We undertook a bibliometric analysis of Australian rural research trends and compared these with international RH research output, and analyzed how Australian RH research has been addressing the National Health Priority Areas (NHPAs) during this period. METHODS: Medline-listed publications from 1990 to 2005 relating to rural health or rural health services were downloaded using PubMed and written to a Microsoft Access database using specially developed software. Analysis was performed to determine the country of origin of the authors, frequency of journals, publication types and how publications addressed Australian NHPAs. RESULTS: We retrieved 20 913 rural health publications of which 1442 (6.8%) were from Australia. Analysis from 1990 and 2005 showed total world yearly publications increased from 410 to 1207, while the respective contribution from Australia increased from 17 (4.1%) to 198 (16.4%). Canadian and USA contributions increased respectively from 10 (2.4%) to 110 (9.1%) and 131 (32%) to 298 (24.7%). The top five journals that published RH articles were Journal of Rural Health (JRH; 453), Australian Journal of Rural Health (AJRH; 417), Medical Journal of Australia (MJA; 192), Social Science Medicine (191) and Lancet (171). The Australian journals with the largest number of RH publications were AJRH (374), MJA (177), Australian Family Physician (101), Rural Remote Health (55) and Journal of Telemedicine Telecare (54). The most frequent publication type was the journal article in all three countries. Australian publications comprised journal articles (85.1%), letters (9.1%), reviews (5.6%), editorials (4.7%) and clinical trials (2.9%). Australia had the lowest proportion of clinical trials of the three countries. Of the total 1290 Australian publications, 317 (25%) addressed the NHPAs. Of these, 118 (37.2%) addressed mental health, 54 (17%) cancer, 41 (12.9%) cardiovascular disease, 37(11.7%) injury prevention, 35(11%) diabetes and 15 (4.7%) arthritis and musculoskeletal conditions. DISCUSSION: Australia's contribution to the international RH literature is increasing, both in terms of the relative numerical contribution and the prominence of selected Australian journals as the destination for articles on RH topics. Of dedicated RH journals, AJRH is now almost as frequently used by authors as JRH. However the general journals Lancet, BMJ and MJA were also among the most frequent publishers of RH articles. Telemedicine and general practice journals (Australian Family Physician & Canadian Family Physician) were also among the top journals that published RH articles, which highlights the increasingly prominent role played by information and communication technologies in the delivery of rural health care in general practice settings. The most frequent NHPA addressed by the RH publications in Australia was mental health. However only approximately 1% of total Australian health publications from 1990 to 2005 addressed RH. There is still a pressing need for more RH research, particularly in health priority areas.
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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 | 0.056 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.178 | 0.299 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".