Commentary on the harm reduction reference group of justice health and forensic mental health network and corrective services NSW, Australia
Bibliographic record
Abstract
PURPOSE: This commentary aims to reveal how a steering committee has effectively responded to advancing accessibility to harm reduction resources, hepatitis C virus (HCV) policy and health strategies within adult prison settings in New South Wales (NSW). DESIGN/METHODOLOGY/APPROACH: By reviewing the audit approach taken by the of the Justice Health and Forensic Mental Health Network and Corrective Services New South Wales Harm Reduction Reference Group (JHFMHN/CSNSW HRRG), this commentary emphasizes the committee's success in identifying contemporary harm reduction issues that affect people in custodial settings. This commentary is a compilation of data gathered through the 2018 JHFMHN/CSNSW HRRG audit and corresponding program materials. Conclusions regarding the effectiveness of the working group's audit were drawn by critically appraising the JHFMHN/CSNSW HRRG's Final Audit Report (JHFMHN and CSNSW, 2018) with reference to current harm reduction literature. FINDINGS: The HRRG has provided leadership, professional representation and strategic advice on the development, implementation, monitoring and evaluation of best practice harm reduction strategies in prison settings. The HRRG developed and maintained networks and information exchange between the state-wide HCV health network, corrections services and the NSW harm reduction sector at large. Public health partnerships and advocacy that involve all key players, such as the HRRG, will continue to be crucial to remove barriers to enhancing HCV harm reduction measures especially in NSW prison settings. SOCIAL IMPLICATIONS: Strategies such as primary prevention and treatment can mitigate the spread of HCV in the custodial system. This audit of access to harm reduction resources was conducted on behalf of the diverse group of professionals, scholars and stakeholders comprising the HRRG. This audit and other advocacy efforts of this committee can facilitate future access to quality healthcare and the necessary policies required to support a healthier prison population at large. ORIGINALITY/VALUE: Collaborating with health authorities, researchers and social service workers can enable prison health-care systems to be guided by wider health workforce programs and public health standards. This collaboration can reduce the professional isolation of custodial health-care staff and promote a balanced approach to harm reduction policies by ensuring an equitable focus on both health and security imperatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".