A Postcolonial Discourse Analysis of Community Stakeholders’ Perspectives on Supporting Urban Indigenous Older Adults to Age Well in Ottawa, Canada
Bibliographic record
Abstract
The urban Indigenous older adult population in Canada continues to grow; however, there is a lack of understanding of how non-Indigenous health and social services and Indigenous-specific organizations are responding to and addressing the growth of this population. Therefore, in this research, we conducted a postcolonial discourse analysis of semi-structured interviews with six decision-makers (e.g., managers and directors of health and social services organizations) and seven service providers (e.g., program coordinators and social workers) from Indigenous and non-Indigenous health and social service organizations in Ottawa, Canada, to examine how they produce understandings of supporting urban Indigenous older adults to age well. The participants produced three main discourses: (a) non-Indigenous organizations have a responsibility to support Indigenous older adults, (b) culturally specific programs and services are important for supporting Indigenous older adults to age well, and (c) it is difficult for community stakeholders to support Indigenous older adults to age well because this population is hard to reach. The results demonstrate the complexities and tensions that community stakeholders face in supporting Indigenous older adults to age well within a sociopolitical environment informed by reconciliation and a sociodemographic trend of an aging population.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".