Exploring the Lived Experience of Aging among Chronically Homeless Older Adults
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,007 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».