Health Justice Partnerships (HJP) – why in the UK? Australia – what can we learn at home here in the United Kingdom?
Bibliographic record
Abstract
Legal Empowerment and tackling complex problems and inequality - Health Justice Partnerships . My work and passion for improved outcomes by HJP continue. Multidisciplinary integrated practice (adding justice to the holistic care repertoire) makes inroads into social determinant of health outcomes. In the UK and Canada, I have been advising on and presenting on HJPs since 2016. I loved recently collaborating with Central England on articles on Health Justice Partnerships and trusted intermediaries. Talking at the Legal Action Group Conference on HJP (its CEO Sue James also loves HJP and did a Churchill Fellowship on them) last week and being on a panel with Sue, Ruth Mercer from Southwark Law Centre and Emma Austin from Central England Law Centre was so energising. With the 10-year NHS Strategy HJP should not be ignored but should be an underpinning. Strong vantage point as researcher, evaluator, practitioner, adviser, implementer, so, knowing how to make it happen at an operational level.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.020 |
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".