Exploring the Lived Experience of Aging among Chronically Homeless Older Adults
Bibliographic record
Abstract
Introduction/background summary: Twenty-four percent of Canada’s homeless shelter users are over the age of 50 years, a number that is predicted to rise [1]. Homeless older adults experience chronic health issues. They encounter barriers to accessing and utilizing healthcare and social services, such as stigma and misalignment between their needs and available services. Why did you do it? Designing healthcare and social services to address the needs of older adults aging with homelessness first requires evidence of their experiences. This study explores the conceptualization of older chronically homeless adults’ lived experiences related to aging. Who is it for? The research is for healthcare and social service providers and policymakers, populations that are homeless, researchers, and advocates interested in issues of homelessness. Who did you involve and engage with?Collaboration will occur with staff members and research participants in the study setting. Staff offer insights into participants’ recruitment, engagement and retention. Participants provide feedback on the interview questions, their transcribed interview notes, and written descriptions of their stories. Knowledge translation products and actionable items to improve service delivery will be co-created with the study settings. What did you do? The phenomenological research explores the aging experiences of older adults living with chronic homelessness. Participants will be recruited from four social service organizations and screened using the eligibility screening survey. Twenty eligible participants will be selected from the screening for data collection. The interview questions will be piloted with participants. Qualitative data collection occurs through face-to-face interviews and unstructured observations of participants in the study settings. Quantitative descriptive data will be gathered using the demographic survey and the SF-12 Short Form Health Assessment Survey. The NVivo software will be utilized to analyze the qualitative data. Quantitative data analysis will be completed with the SPSS statistical software. What results did you get? What impact did you have? Expected results include evidence of participants’ early physiological and psychosocial changes related to aging, healthcare and social service unmet needs and service utilization, and strategies utilized to manage developmental tasks related to aging. This study contributes new knowledge on the aging experiences of older adults living with chronic homelessness. It helps to inform healthcare and social service policy and emerging practices, including person-centred care and integrated healthcare. What is the learning for the international audience?The results will be transferable or interesting to international audiences that are involved in areas of research, policy, practice and advocacy related to homelessness. What are the next steps? The next steps involve knowledge translation at conferences, symposiums, presentations and publications. Researchers will collaborate with the study settings to translate the research findings into actionable policy, service design, and delivery mechanisms. References 1.Gaetz S., Dej E., Richter T., & Redman M. (2016). The State of Homelessness in Canada 2016. Toronto: Canadian Observatory on Homelessness Press.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".