Empowering Community-Dwelling Older Adults to Become Self-Advocates for Health Care Services
Bibliographic record
Abstract
Health care systems are shifting to patient-centered models of care, requiring individuals to be active participants in health care interactions. Health self-advocacy is the ability to state one’s needs and take action to meet these needs, through activities such as communicating with health care providers, making health care decisions, and obtaining relevant information. Older adults in Newfoundland and Labrador have high rates of chronic disease and frailty, but as frequent consumers of health care services, there is limited understanding of how they self-advocate in health care settings. This purpose of this research study was to understand the perspectives of older adults in Newfoundland and Labrador about the health care system and self-advocacy. From this information, a health self-advocacy workshop for community-dwelling older adults was developed, implemented, and evaluated. This study used an exploratory sequential mixed methods design. In phase one, eighteen older adults across Newfoundland and Labrador participated in interviews and focus groups to provide detailed perspectives about health self-advocacy. Nine components of health self-advocacy for older adults were identified, including knowledge of rights, communication, informed decision-making, persistence, self-management, health literacy, computer literacy, obtaining relevant information, and connected strength. In phase two, synthesis of the data collected, and existing self-advocacy frameworks and learning theories lead to the development of a self-advocacy workshop for older adults. In phase three, the workshop was implemented and evaluated, using a single-group, pretest-posttest design. Fourteen older adults in an urban Newfoundland community participated. The evaluation indicated positive changes in self-reporting of self-advocacy skills, with the greatest change in scores found in finding health information and accessing community groups to support health care. Future directions for the workshop include exploring applicability to virtual, rural, and more diverse populations of older adults. The findings from phases 1 and 2 led to the development of a new self-advocacy framework, the Health Self-Advocacy Framework for Older Adults. The framework describes how previous experiences and specific components support older adults in self-advocacy. Further research is required to develop the framework and understand the relationship of the identified components in supporting self-advocacy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".