Applying the Population Health Management Maturity Index in the Canadian context
Bibliographic record
Abstract
There is a demand from regional health systems and teams for practice oriented guidance where to start and what is needed in the process of applying PHM. The Population Health Management Maturity Index (PHM-MI) is developed as an international tool to support regions in choosing their next steps. The PHM-MI is based on international scientific literature and a rigorous process with Dutch and European expert opinion input. Next, the tool was piloted in an Australian region in 2022. From 2023 onwards a next iteration of the development process has been set in place in the Netherlands. Within this iteration, potential end-users were invited to jointly make the tool suitable for practical application. Particular topics that were discussed and adapted were: Language ambiguity, fitting the process into the strategic planning of the region, the need for adaptation to local context, and dashboard development. In this workshop we want to explore the best way to apply the PHM-MI in the Canadian context to inspire local health systems to improve population health. In this phase of development, the tool is focussed on health commissioners, executive directors, project leaders and managers from all organizations involved in health and social services in the region. In addition, part of the tool is focussed on raising awareness and instigate co-design with health professionals and the community within the PHM approach of the region. We would like to engage with potential end-users and researchers around this topic from the Canadian context and internationally. After a short explanation of the tool and its development so far (0min), we want to invite participants to join the discussion in a world caf setting (3 rounds of 5min). Main topics we would like to discuss in the small groups are the following: ) How to set the scenefor use of the PHM-MI, how should it be introduced? 2) People in which role have the best overview of the region to analyse the region starting point using the PHM-MI? 3) How can the PHM-MI be fitted into the strategic planning process of the region to use it as an instrument for learning? After these rounds the workshop leaders will close with a short summary of the discussions (5min). The participants can engage in small tables to discuss the main questions. The presenters will summarize the discussion per main question at the end of the session. Afterwards, the results will be shared in a white paper by the workshop leaders.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".