Can We Track the Impact of Australian Mental Health Research?
Bibliographic record
Abstract
OBJECTIVE: Arguments are being made to increase research and development funding for mental health research in Australia. Consequently, the methods used to measure the results of increased investment require review. This study aimed to describe the status of Australian mental health research and to propose potential methods for tracking changes in research output. Specifically, we describe the research output of nations, Australian states, Australian and New Zealand institutions and Australian and New Zealand researchers using citation rates. METHOD: Information on research output was sourced from two international databases (Institute for scientific information [ISI] Essential Science Indicators and ISI Web of Science) and the ISI list of Highly Cited Researchers. RESULTS: In an international setting, Australia does not perform as well as other comparable countries such as New Zealand or Canada in terms of research output. Within Australia, the scientific performance of institutions apparently relates to the strength of some individual researchers or consolidated research groups. Highly cited papers are evident in the fields of syndrome definition, epidemiology and epidemiological methods, cognitive science and prognostic or longitudinal studies. CONCLUSIONS: Australian researchers need to consider the success of New Zealand and Canadian researchers, particularly given the relatively low investment in health and medical research in New Zealand. Although citation analyses are fraught with difficulties, they can be effectively complemented by other measures of responsiveness to clinical or population needs and community expectations and should be conducted regularly and independently to monitor the status of Australian mental health research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".