Is there a missing-middle in Australian mental health care?
Bibliographic record
Abstract
OBJECTIVE: The term 'missing-middle' has been prominent in discourse relating to provision of mental health care in Australia, particularly by proponents of non-governmental youth mental health services such as headspace and related adult services. We investigate whether there is an empirical basis for use of the 'missing-middle' term, founded on qualitative and quantitative research. CONCLUSIONS: Despite the widespread use of the term 'missing-middle' for advocacy in Australia, there is a lack of research characterising the epidemiological characteristics of the group. The validity of advocacy predicated on the basis of the 'missing-middle' care-gap should be reconsidered. Research, such as systematic service mapping and health needs assessment, is a necessary foundation for evidence-based mental healthcare policy, planning and implementation. Without such research, vital government funds may be deployed to 'missing-middle' programmes that may not improve Australian public health outcomes.
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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".