Enabling the participation of marginalized populations: case studies from a health service organization in Ontario, Canada
Bibliographic record
Abstract
We examined efforts to engage marginalized populations in Ontario Community Health Centers (CHCs), which are primary health care organizations serving 74 high-risk communities. Qualitative case studies of community participation in four Ontario CHCs were carried out through key informant interviews with CHC staff to identify: (i) the approaches, strategies and methods used in participation initiatives aimed specifically at engaging marginalized populations in the planning of and decision making for health services; and (ii) the challenges and enablers for engaging these populations. The marginalized populations involved in the community participation initiatives studied included Low-German Speaking Mennonites in a rural town, newcomer immigrants and refugees in an urban downtown city, immigrant and francophone seniors in an inner city and refugee women in an inner city. Our analysis revealed that enabling the participation of marginalized populations requires CHCs to attend to the barriers experienced by marginalized populations that constrain their participation. Key informants outlined the features of a 'community development approach' that they rely on to address the barriers to marginalized peoples' involvement by strengthening their skills, abilities and leadership in capacity-building activities. The community development approach also shaped the participation methods that were used in the engagement process of CHCs. However, key informants also described the challenges of applying this approach, influenced by the cultural values of some groups, which shaped their willingness and motivation to participate. This study provides further insight into the approach, strategies and methods used in the engagement process to enable the participation of marginalized populations, which may be transferable to other health services settings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".