#NavigatingHealth - Building a Global Movement Empowering Patients to Navigate Health Systems
Bibliographic record
Abstract
Background: Whilst health reports talk about health systems shifts from reactive “one size fits all” to proactive personalised health care, the current reality is that health systems are challenging for patients and carers to navigate. Patients frequently experience gaps and barriers in healthcare. We talk about patient-centred care, but we do not have patient centred systems. The challenges are not new and patient navigation was extensively research by Professor Harold Freeman in the US over 30 years ago. Audience: This presentation is for a multidisciplinary audience of patients, health professionals, policy managers and politicians focused on enabling patient-centred at a systems level. Who did you involve and engage with? What was the approach and process undertaken? Patients, professionals and policy makers in Australia and globally have been involved in what started as a national Community of Practice in Australia “Mind the Gap: #NavigatingHealth”. The approach was tested for audience interest with a series of two webinars in September 2021 to set the scene of navigating health across a life journey of patient and provider experiences with a solutions focused approach on how to tackle the many challenges. Following the success and interest of the webinars and as part of a focused advocacy and change management approach we developed a bimonthly Community of Practice. This is open to all those in Australia. Our focus in the first year was to make visible the many solutions being developed to support patients and to develop an engaged participatory community and build connection. Further to the success in the first year, we have now expanded the approach at a global level running our first global webinar in November 2022 with speakers from Australia, the United States of America, Canada and the United Kingdom providing a connected approach across a life journey. This included lived experience of patients as well as presentations from non-governmental, medical and government organisations interweaving micro, meso and macro approaches (for example included American Cancer Society, NHS England). What is the learning for the international audience? Problems that patients face navigating health systems in countries are common and consistent. Collaborating across disease and functional areas both within and across countries allows the opportunity to take a “big-picture” view to take a systems focused approach. This allows the solutions being developed to be coalesced at a systems level, remove duplication and leverage resources to benefit both patients, providers and policy makers to focus on a person-centred value-based healthcare approach across a life journey. What are the next steps? Our next steps are to consolidate the learnings and scale. Whilst our initial focus has been high income countries we will be expanding the approach in 2023 to low and middle income countries using the same methodology.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.035 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".