Identifying superusers of health services with mental health and addiction problems: Putting the Canadian Institutes of Health Research Strategy for Patient Oriented Research into action
Bibliographic record
Abstract
ABSTRACT
 ObjectiveWe were funded by Canadian Institutes of Health Research Strategy for Patient Oriented Research (CIHR-SPOR) to use administrative data to identify people with mental health and/or addiction (MHA) problems, and determine characteristics that lead to them becoming a superuser of health services. The aim of the CIHR-SPOR is to ensure that research improves healthcare systems and practices. Their approach is to fundamentally change the ivory tower nature of research to make it more inclusive, user friendly, and timely. An essential component of the CIHR-SPOR is engaging patients as partners at all stages of the research. In this presentation we will describe the challenges, successes, and failures of engaging patients as part of the research team.
 ApproachIn Saskatchewan, Canada, we engaged with community programs to determine the ‘on-the-ground’ reality of people living with MHA problems. Our discussions revealed that First Nations and Métis People are highly overrepresented in the MHA patient population. For example, they comprise 15% of the Saskatchewan population yet 70% of patients receiving treatment at a local methadone clinic. For this reason, we are focusing our efforts on engaging with this specific patient population.
 ResultsWe encountered a number of barriers to finding patient advisors. First, patient advisors available through institutional programs are not representative of our target population, being more affluent, well-educated, and Caucasian. There are currently no formal avenues to identify and invite members of the target population to join the research team and so we are using personal connections and team building to identify patients advisors. Second, complicated institutional policies on reimbursement for patient advisors impede participation by low income individuals. Third, we are concerned the socioeconomic disparities common in this vulnerable population may impede their full and honest participation as a patient advisor.
 ConclusionsPatients and their family members are invaluable members of the research team. However, a power differential often exists between team members and patients due to socioeconomic differences, including race and education. Stigmatized health conditions such as MHA problems can exacerbate the feelings of a power differential. All of these things can prevent genuine involvement by patients. The most important step in successfully involving these untraditional team members is building trust through unconditional acceptance and respect for each individual’s lived experience. Building trust takes time and so patient engagement cannot be expected to follow a rigid schedule.
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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 | 0.023 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".