A ‘Wholistic Needs’ Tool: The Process of Co-designing a Proactive Screener to Identify and Support Health and Social Needs
Bibliographic record
Abstract
Based in Ontario, Canada, this ongoing co-design initiative aims to develop a Wholistic Needs Pre-Screener for proactive identification and management of health and social needs for the 525,000 people in the Middlesex London Ontario Health Team’s attributed population. The significance of this tool was underscored in prior co-design efforts, where patients, caregivers, and providers identified several key themes for improvement in healthcare within the Ontario context. These themes included the need for improved access to services, early diagnosis, and more self-management. These opportunities formed the purpose of developing the Wholistic Needs Pre-Screener. A literature review entailing 24 different screeners, assessments and tools was completed to ensure the development of the pre-screener was novel in the local and national context. Through additional co-design, the stakeholders co-developed the definition of health for the purpose of the screener, the function of the screener and the vision of how it could work. The co-designed definition of health spans across eight aspects of patient wellness: physical, spiritual, mental, emotional, environmental, social, cultural, and economic wellness. These domains have been developed by the National Collaborating Centers for Public Health (NCCPH) & National Collaborating Center for Determinants of Health (NCCDH). The co-designed functions of the screener include the ability to screen the wholistic health of patients using 10 questions or fewer to enable reaching the full population and increased awareness and access to services through the navigation pathways that will be triggered by the screener questions. The navigation pathways are being developed through leveraging various stakeholder groups including the Navigators Collaborative and the London Middlesex Primary Care Alliance, to improve both the patient and provider navigation experience. This Ontario-based tool will be a steppingstone for integration through a proactive and wholistic approach. The tool’s potential to screen the health and social care of an attributed population aligns with the goals of global health initiatives and the nine pillars of integrated care identified by the International Foundation for Integrated Care. Upon further development of the Wholistic Needs Pre-Screener initiative, the opportunities for innovative knowledge exchange that can occur between countries, populations, and communities are expansive. Ensuring the screener can be used locally, nationally and internationally has been integral in its development. As such, the project team has been and will continue to document the process of the localization of the associated navigation pathways, so that this screener can be replicated and adapted to fit the needs of national and international populations.
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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 | 0.078 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".