Heard and valued: the development of a model to meaningfully engage marginalized populations in health services planning
Bibliographic record
Abstract
BACKGROUND: Recently, patient engagement has been identified as a promising strategy for supporting healthcare planning. However, the context and structure of universalistic, "one-size-fits-all" approaches often used for patient engagement may not enable diverse patients to participate in decision-making about programs intended to meet their needs. Specifically, standard patient engagement approaches are gender-blind and might not facilitate the engagement of those marginalized by, for example, substance use, low income, experiences of violence, homelessness, and/or mental health challenges-highly gendered health and social experiences. The project's purpose was to develop a heuristic model to assist planners to engage patients who are not traditionally included in healthcare planning. METHODS: Using a qualitative research approach, we reviewed literature and conducted interviews with patients and healthcare planners regarding engaging marginalized populations in health services planning. From these inputs, we created a model and planning manual to assist healthcare planners to engage marginalized patients in health services planning, which we piloted in two clinical programs undergoing health services design. The findings from the pilots were used to refine the model. RESULTS: The analysis of the interviews and literature identified power and gender as barriers to participation, and generated suggestions to support diverse populations both to attend patient engagement events and to participate meaningfully. Engaging marginalized populations cannot be reduced to a single defined process, but instead needs to be understood as an iterative process of fitting engagement methods to a particular situation. Underlying this process are principles for meaningfully engaging marginalized people in healthcare planning. CONCLUSION: A one-size-fits-all approach to patient engagement is not appropriate given patients' diverse barriers to meaningful participation in healthcare planning. Instead, planners need a repertoire of skills and strategies to align the purpose of engagement with the capacities and needs of patient participants. Just as services need to meet diverse patients' needs, so too must patient engagement experiences.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".